{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimating the SCC using CIL damage functions and the FAIR SCM\n",
    "\n",
    "The experiment is conducted as follows:\n",
    "\n",
    "* Use the RCP scenarios as defined by the default FAIR model\n",
    "* Add an additional CO2 impulse (1 Gt C) to each trajectory in 2020\n",
    "* Compute damages using the resulting temperature trajectories\n",
    "* Subtract the damages in the standard RCPs from the damages in the pulse runs\n",
    "* Divide this value by the quantity of added CO2 (1 Gt C * 44.0098 / 12.011 = 3.66 Gt CO2) to achieve \\$/ton CO2\n",
    "* Compute the NPV of this time series of marginal damages using various discount rates\n",
    "\n",
    "Note: This notebook allows users to generate the point estimate SCCs along with damage function uncertainty. \n",
    "Computing the climate uncertatinty required to compute the full uncertatinty SCCs requires the use of servers to run 100k simulations.\n",
    "Code for this is provided in the `full_uncertainty_ensemble` notebook.\n",
    "\n",
    "Note: Damage function units fed into this calculation should be billions of 2019 USD\n",
    "\n",
    "#### Please activate the `mortalityverse` environment when running this notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: fair==1.3.2 in /home/sklos/miniconda3/lib/python3.8/site-packages (1.3.2)\n",
      "Requirement already satisfied: scipy>=0.19.0 in /home/sklos/miniconda3/lib/python3.8/site-packages (from fair==1.3.2) (1.6.2)\n",
      "Requirement already satisfied: numpy>=1.11.3 in /home/sklos/miniconda3/lib/python3.8/site-packages (from fair==1.3.2) (1.20.3)\n"
     ]
    }
   ],
   "source": [
    "! pip install fair==1.3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from matplotlib import cm\n",
    "import seaborn as sns\n",
    "plt.style.use('seaborn-white')\n",
    "plt.rcParams['figure.figsize'] = (16, 9)\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import sys\n",
    "sys.path.append('./functions/.')\n",
    "import load_fair\n",
    "import scale_ag02_scc\n",
    "\n",
    "from scipy.stats import lognorm, norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "REPO = os.getenv('REPO')\n",
    "DB = os.getenv('DB')\n",
    "OUTPUT = os.getenv('OUTPUT')\n",
    "\n",
    "\n",
    "# mortality specific parameratization for code below\n",
    "version = 'v0.5' # code version\n",
    "specification = 'mortality_quadratic_IGIA' # sector specific tag\n",
    "ssp = 'SSP3'\n",
    "MAGNITUDE_OF_DAMAGES = 1e9  # magnitude of damage function values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_data = '{}/4_damage_function/'.format(DB)\n",
    "OUTPUT_data = '{}/5_scc/global_scc/quadratic'.format(DB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Code toggles\n",
    "\n",
    "# point estimate (`point-est`) or df uncertainty (`df-uncertainty`) (note: df uncertainty will not generate some plots)\n",
    "scc_output = 'point-est' \n",
    "\n",
    "# damages including or excluding adaptation costs (damages vs wo_costs)\n",
    "costs = 'damages'\n",
    "\n",
    "# generate plots or not\n",
    "generate_plots = True\n",
    "\n",
    "# Run option: whether or not to hold the post-2100 damage function constant.\n",
    "hold_2100_damages_fixed = False # NOTE: False is the default setting used in the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if scc_output == 'point-est':\n",
    "    index_cols = ['year', 'age_adjustment', 'vsl_value', 'heterogeneity']\n",
    "    file_infix = ''\n",
    "elif scc_output == 'df-uncertainty':\n",
    "    index_cols = ['year', 'age_adjustment', 'vsl_value', 'heterogeneity', 'pctile']\n",
    "    file_infix = 'quantilereg_'\n",
    "    specification = '{}_{}'.format(specification, scc_output)\n",
    "else:\n",
    "    raise NotImplementedError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load FAIR Temperatures Anomaly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RCPs\n",
    "\n",
    "We can run FAIR with the CO$_2$ emissions and non-CO$_2$ forcing from the four representative concentration pathway scenarios. To use the emissions-based version specify ```useMultigas=True``` in the call to ```fair_scm()```.\n",
    "\n",
    "By default in multi-gas mode, volcanic and solar forcing plus natural emissions of methane and nitrous oxide are switched on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SCC\n",
    "\n",
    "We can compute the SCC by adding an additional pulse in CO2 emissions to the RCP trajectory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario design\n",
    "\n",
    "This study uses a 100 Gt C emissions pulse. You can change the pulse amount by modifying the `PULSE_AMT` variable below, and adapting the `CONVERSION` value to match. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "PULSE_YEAR = 2020  # year in which pulse will be emitted. Update the PULSE_DEFLATOR accordingly\n",
    "PULSE_AMT = 1.0  # in Gt C = 1e9 ton C\n",
    "\n",
    "# CONVERSION is in units of [pulse/tCO2] = [1 pulse/PULSE_AMT GtC * 1 GtC/1e9 tC * 12tC/44tCO2]\n",
    "# This is used to convert costs ($Bn_2019 / pulse) to SCC ($_2019/ton CO2).\n",
    "# Therefore, it should be the inverse of any changes to PULSE_AMT\n",
    "CONVERSION = 1.0 / PULSE_AMT / 1e9 * 12.011 / 44.0098"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished loading FAIR\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make_plots = True generates Appendix chart G1 \n",
    "fair_temperatures_anomaly = load_fair.temperatures_anomaly(PULSE_YEAR = PULSE_YEAR, \n",
    "                                                           PULSE_AMT = PULSE_AMT,\n",
    "                                                           make_plots = True, \n",
    "                                                           output = OUTPUT_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incorporate a single damage function\n",
    "\n",
    "This is the damage function generated in th `4_damage_function` step, with a set of coefficients for each year.\n",
    "\n",
    "Note: The damage function can be held constant or allowed to vary post-2100. \n",
    "\n",
    "Note: the damage functions passed have columns for `anomalymin` and `anomalymax` which are not used, so they are dropped after reading in the .csv files below. In dropping them, the parameters fit to the damage function are given by `coeff_names` as defined here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Time the SCC calculation\n",
    "startTime = datetime.now()\n",
    "\n",
    "# damage function coeffs filepath \n",
    "if costs == 'damages':\n",
    "    fp = os.path.join('{}{}/mortality_damage_coefficients_quadratic_IGIA_MC_global_poly4_uclip_sharecombo_{}{}.csv'\n",
    "                      .format(INPUT_data, costs, file_infix, ssp))\n",
    "elif costs == 'wo_costs':\n",
    "    fp = os.path.join('{}{}/mortality_wo_costs_coefficients_quadratic_IGIA_MC_global_poly4_uclip_sharecombo_{}{}.csv'\n",
    "                      .format(INPUT_data, costs, file_infix, ssp))    \n",
    "\n",
    "def read_damage_func(fp, index):\n",
    "    df = pd.read_csv(fp, index_col=index)\n",
    "    df.columns.names = ['coeff']\n",
    "    return df.stack('coeff').to_xarray()\n",
    "\n",
    "ds = read_damage_func(fp, list(range(len(index_cols)))).to_dataset(name='SSP3')\n",
    "\n",
    "# Drop 'anomalymin' and 'anomalymax' from the coeff list. Only keep the constants and the betas.\n",
    "ds = ds.sel(coeff=[c for c in ds.coeff.values if 'anomaly' not in c])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following operation reorders the 'variable' and 'year' coordinates.\n",
    "coeffs_all_years = (\n",
    "    ds.to_array(dim='variable').to_series().unstack('year')\n",
    "    .reindex(list(range(2020, 2301)), axis=1)\n",
    "    .stack('year')\n",
    "    .unstack('coeff')\n",
    "    .to_xarray()\n",
    ")\n",
    "\n",
    "if hold_2100_damages_fixed:\n",
    "    mask = coeffs_all_years['year'] > 2100\n",
    "    coeffs_2100 = coeffs_all_years.sel(year=2100)\n",
    "    coeffs_all_years = xr.where(mask, coeffs_2100, coeffs_all_years)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3Sy726qCTJk2iSpUqvPPOOwA4ODiwd+9e7ddjYmJo1KiR9s1V7dq1tW+u7O3tSUhI0B774O2XEEL30rJyeHvlYY5FJvCtb2M61a+g65BKlc9/PsvasBsc+bg9tuXyE7/opAzqVsyfHWF/Pxl84EpsCk0+/w2lQkHEl52LPV4hROHo0KEDH374ITt27CArK4tp06ZRp04dJkyYwMaNG3F0dKRr164YGhrqzfY1QoiH9lyIYcrW00TeS+etJs5M7lyb8uWMnnxiESvWJHDbtm0YGhry7rvvattcXV35+OOPSUpKQqVSER4ezuTJk0lJSSEkJAQvLy/tmytDQ0OqVavGkSNHcHNzY+fOnfTt27c4b0EI8RgZ2bkMXnWEw9fu8b9ejXitYUVdh1TqXIpJAeDb3Zf45PW6KIC4lMy/jQQ+/IXS7CVrDl/LLwyTd38WhawBEqJksrS0JCAg4JH2FStWPNKmL9vXCCHgTmIGn/18hu2n7lDdzoz1QzxoUV1/qqQXWRJ4+vRpZs2axc2bNzEwMGDHjh3cvXsXIyMjbeJWvXp1pk2b9kxvriZPnsyUKVPIy8vD1dWVli1bFtUtCCGeQkZ2LkNWHyH0yl3m+rjSpZGTrkMqlR6s+Vsdeh1rUzX+7pXJ04DD/Spi5Yweduc93Sppk0CA2JRM7XRRIYQQQhSd3DwNq0OvMXfnRbJy8xjboSZD21TDyEC/1uoXWRJYv359goKCnurYZ3lz5eLiwrp16wolRiHEi8nMyWXEmqPsvxTH7Lca0r2Js65DKrWS0rNxdbYkTwOHrt6j3f0qYg73k7u/j/R1a+zEuE0ntZ9HxKRIEiiEEEIUsZNRCXy05TSnbibiVcOWz7vU5yVbM12H9VjFXhhGCFE6ZOXkMWrtMfZciOXLbg3o2aySrkMq1ZLSs7E0VVO3ogUXopO587eN4v/JQKXkKx9XalfIX3AecX8qqRBCCCEKX3JGNtO2naHrwr+4k5TBfL/GrH67ud4mgKCDwjBCiJIvOzePd9cfY9e5aD7rUg9/98q6DqnUS8rIoXJ5M2pVMGfjkUjO3EoCwMHy4VrA395vTe79qsk9mjrzVhMnGn/+GxsORfLnpTi+6umKhbGUhhdCCCEKg0ajYfupO3z60xliUzLp416FDzvWwtJE/3/XShIohHgmObl5vLfxOCFn7jDl9br0a/GSrkMqExLTs7EwNtCO7u27GItKqaC82cMksMY/Sk0rFAperV+R9YducPZ2Ej+fuC0JuxBCCFEIIu+l8cnW0+y9EEvdihZ818+NRpWsdB3WU5PpoEKIp5abp2Fs8Al+OXmbyZ1r83arqroOqUzQaDT500FNDKl1Pwk8EZlAZRtTVMr/rvrZo+nDdZo/nbhVpHEKIYQQpV1WTh6L9kbQ4X/7OHT1Hh+/Vodt73iWqAQQZCRQCPGU8vI0jN90kq3HbzGuYy2Gtq6u65DKjLSsXHLyNFiYGFK+nBG25YyIS8mkul25J57btIo1P4xoyd4LMSzYE8HskPNYmhjSx6MKZkbyK0AIIYR4Woev3eOjLae4GJ1Cx3oOTH2jHo5WJroO67nIXwBCiCfKy9MwafMpfgiP4v32NRn1souuQypTkjKyAbRrDGpXMOfPiExqODw5CYT8RNDa1JD5v0ewaO9lAMyNDWVqqBBCCPEU4lOzmPnreTYeicTJyoTAfm60r+ug67BeiCSBQoj/pNFo+GTraTYeiWR0OxfGtK+h65DKnKT0HABtUZf8JDAOl6cYCXygml05nKxMuJmQjtpAyelbiUUSqxBCCFFaaDQaNh2N4svt50jKyGFY62qMaV8DU3XJT6FK/h0IIYqMRqNh2rYzrA27wfA21fmgQ01dh1QmJaYXHAms62gBoF0f+LR+Gt0KjUbDyLXhnL1fXVQIIYQQj4qISeajLacJu3qPJpWt+LJ7A2pXsNB1WIVGkkAhxGNpNBq++OUcq0KvM7hVVSZ0qlVgQ3LxqC+//JITJ06gUCiYPHkyDRs2LJTrJt1PAi1M8rvsN1wdsTQxpJ7js/0ysjFTA1DP0ZJ1h66Tm6d5YmEZIYQQoizJyM5lwe8RLP3jMiaGKmZ0b0Avt0ooS9nvS0kChRCP0Gg0zAw5z7I/rzKg5Ut89FodSQCf4NChQ1y/fp2NGzcSERHBpEmTCA4OLpRrP9gY3s48fzsIQ5US7zrPvxahrqMFGdl5XI1LxcX+6aeUCiGEEKXZHxdj+WTraa7fTaNbYyc+eq0OtuWMnnxiCSRJoBCiAI1Gw9ydF1m67wp9PCoz9Y26kgA+hdDQUNq3bw+Ai4sLSUlJpKSkUK7ciydZkfFpqFVKHMyNX/hagDbxuxybIkmgEEKIMi8mKYPPfj7LzydvU9XWjLWD3fF0sdV1WEVKkkAhRAHf7L7Egj0R+DWvxGdv1pcE8CnFxcVRr1497efly5cnNja2UJLAqHvpOFmbFNpUlGp2ZgCcu51E21p2GBmoCuW6QgghREmSm6dhXdh1ZodcIDMnj/fa12B4m+oYG5b+34uSBAohtBbuiWDerkv4NHVmetcGpW7+e1HSaDSPfF5YCXRkfBrO1oW3D5GFsSF25kbM23WJnWei2T7Gq9CuLYQQQpQEp28m8tGPpzkRmYCnS3k+71Kfas9QdbukkyRQCAHA0n2XmbPjAt0bOzHzrYaSAD4jBwcH4uLitJ/HxMRga1s4U0lu3EvjtQYVC+VaD6juJ6hnbyeRmZMro4FCCCHKhJTMHP7320VW/HUVGzM183o1oksjxzI380mSQCEEgfuvMOPX87zh6sgcH1epGPkcPD09mT9/Pr6+vpw9exZ7e/tCmQqanJFNQlo2lWxMCyHKhxwsjbUFZ45ciyctK5f2deyf6Zfg2rDr3IxPx8HCmK6NnbRbWAghhBD6RqPRsONMNJ/+dIbbiRn4u1dmQsfaWJqWzd9dkgQKUcatOnCNL345R+cGFfhfT0kAn1eTJk2oV68evr6+KBQKpk6dWijX/eXkbQAaOlkWyvUeWNy7CYev3WPMhuP0DgwDYHaPhvR0q/RU5+fmafhoy2nt56dvJjLHx7VQYxRCCCEKQ1R8GlO3nmH3+RhqVzBngX8Tmlax1nVYOiVJoBBl2Nqw60zddoZX6jrwjW9jDFRKXYdUon344YeFfs3AP6/S0NmSFtXLF+p1Ha1M6NLIieAjUfwZkT+N9asdF3irifNTvQg4dTMRgNY17cjOySP4aBR9PKrgWsmqUOMUQuTLyMjgtddeY9SoUbRo0YLx48eTm5uLnZ0dc+bMQa1Ws23bNlatWoVSqaRXr1706NGD7OxsJk6cyK1bt1CpVMyYMYNKlZ7uZY8QJV12bh7L/7zKvF2XAJjcuTYDPatiKH/vIE9AiDJq4+EbfLTlNN617Vng30Q6RD2k0Wi4FpdKKxfbIlurMO3NepQ3U9PTzZmY5Ez2nI8h9PJdsnPz/vO8Py7GolDAvF6N+K5fU+zMjfj0pzOPFMgRQhSOxYsXY2VlBcC3336Lv78/69atw8nJiU2bNpGWlsbChQtZuXIlQUFBBAYGkpCQwM8//4yFhQXr169nyJAhzJ07V7c3IkQxOXr9Hm/M/5MZv57H08WWXWPbMLR1dfl75z55CkKUQZuORjFx8yna1LRjUZ8mqA2kK9BHyZk55ORpsDFTF9n3cLEvx9FPOvDx63UBGLz6CH4BB5my9Qz3UrOIT8167Hl/XIylgZMlNmZqzI0NebedC+E3EjgZlVhksQpRVl2+fJmIiAjatm0LQFhYGN7e3gB4e3sTGhrKiRMnaNCgAebm5hgbG+Pm5kZ4eDihoaF06NABgFatWnH06FFd3YYQxSIhLYtJm0/x1uJQEtOzWdq3KYH93XCyKrwq26WB/OUnRBmz9fhNxm06gWd1W5b2bSpVIfVQRnYuv5y8zb2U/ATM2rToksAHLIwN6dygAtXtzHilrgPrD92gyee/MWDl4UeOTcrI5lhkAq1r2GnbujZ2wsRQxcI9EeQ8YRRRCPFsZs2axcSJE7Wfp6eno1bn9wt2dnbExsYSFxeHjY2N9hhbW9tH2lUqFUqlkqysx7/cEaIk02g0bDkWhffcfXx/JJLBraqy64M2dKxXQdeh6aUiTQIvXrxI+/btWbNmDQC3b9+mb9+++Pv7M2bMGG0ntG3bNt566y18fHzYtGkTANnZ2YwdOxY/Pz/69OlDZGQkAOfPn8fX1xdfX99CK7wgRFnx88lbvL/xOB5VyxPQz61MbIZaEh26eo9R68LZf3+tnrVZ8VQum+/XhF0ftGFxn6a0qZmf4J2ITCArp2BSt+d8DLl5GlrXfJgEmhsb8k47F3aejWbOjgvFEq8QZcGPP/5Io0aNCqzj+/v08AdTsP9tr9Ki3MNUCH1xJTaF3oFhvL/xBJVsTNn2jicfv14XMyMpf/JviiwJTEtL4/PPP6dFixbatsKYwz59+nQmT57Mhg0bSEhIYN++fUV1C0KUKr+eus2YDcdxq2LDsgFumKglAdRXD6Z/nr4/tbI4RgIBVEoFCoUClVLBsv5uzOjeID+OWw+neOblafjujytUtTV7pLLaqJdd6N7EiZUHrhFzf/sJIcSL2bt3L7t376Znz54EBwezaNEiTExMyMjI/xmLjo7G3t7+sXuV2tnZ4eDgQGxsLJD/gl2j0WBoWDZL4ovSJyM7l//9dpFO8/Zz6mYin3etzw8jWlLPsXArapdGRZYEqtVqAgICsLe317a96Bz2rKwsbt68ScOGDQtcQwjx33aeucPo9cdoVMmK5QObYaqWN2P6zPH+uoWzt5OA4ksC/85ApcS7dn7/vfFQJLl5GjQaDUODjnDmVhKj27k8toroyLbVyczJY8fZ6OIOWYhSad68efzwww98//33+Pj4MHLkSFq2bMmOHTsA2LlzJ15eXri6unLq1CmSkpJITU0lPDwcNzc3PD09CQkJAWDPnj24u7vr8naEKDR/Xorj1W/2883uS3SqX4HdY9vQ16OKbHX1lIrsL0EDAwMMDApe/kXnsMfFxWFhYaE99sE1hBD/7vfz0YxaF049J0tWDmxGOZkaofesTQ0xMlBy5v4InHURFob5L/YWxnRr7MTGI5F4VLehkrUpu87F8H77mnRr7PTYc6rblcPGTM3JyATwqFK8AQtRRowePZoJEyawceNGHB0d6dq1K4aGhowdO5ZBgwahUCgYNWoU5ubmdO7cmQMHDuDn54darWbmzJm6Dl+IFxKbnMn0X87y4/FbVClvyuq3mxdYniCeTrH+Nfiic9gf1yaE+Hf7LsYyPCic2hUsWP12c8yNZQpQSaBQKHC0MuFqXCoqpQILY90l7nN9XPn9fAwHL99jf24c5YwMGOxV9V/XFCkUCho6W0qVUCGKwOjRo7Ufr1ix4pGvd+rUiU6dOhVoe7A3oBAlXV6ehvWHbzDr1/OkZ+fybjsXRr7sIvUNnlOxVgd90Tns9vb2JCQkaI99cA0hxKP+iohj6OojuNiXI2hQcyxNJAEsSSpaGgP5o4K6LOKgVCpwq2LN7vPR/HzqNl0bOz5xoX1DZysuxiSTkplTTFEKIYQozc7dTuKtJQf4aMtp6jpa8OuY1nzwSi1JAF9AsSaBLzqH3dDQkGrVqnHkyJEC1xBCFBR6+S6DVh2mqq0Zawa7Y6WDNWXixVS0zF8XqA//3zV9yZq4lCyycvLwb/7kKZ5tatqi0cDGw5Hk5cmMDSGEEM8nLSuHL7ef4/X5f3L9bhpzfVxZP8QDF/tyug6txCuyOUanT59m1qxZ3Lx5EwMDA3bs2MFXX33FxIkTX2gO++TJk5kyZQp5eXm4urrSsmXLoroFIUqkw9fuMWjVYSpZm7JmsHuRbjQuik6jylb8EB5FQyfdVzjr1tiJiOgUKloZU9fR4onHN6lsTaNKVnz+81nCrtzlu35uxRClEEKI0uS3s9FM23aGmwnp+DarxMRXa+vFi9HSQqEpZQvroqKi8Pb2Zvfu3Tg7O+s6HCGK1dHr8fRbFoaDpTEbhnpgb26s65DEfc/TN2Xl5GGoUpTIPb3O3U7i7ZWHuZ2YwV8T2+F0v+KpEEL/yN9OQp/cSkhn2rYz7DwbTS0Hc6Z3q4/bSzZPPlE8k2KdDiqEKDrHIxMYsPwQduZGrB8iCWBpoDZQlsgEEKBORQu+H9YChQKCj0TqOhwhhBB6Lic3j8D9V2j/9T7+uBTLhE61+fndVpIAFhGpFS9EKXD6ZiL9loVhZWbIuiEeOFhIAih0r5KNKa1cbAncf5Umla3xqmFbYpNaIYQQRefYjXgmbznNudtJvFzLjs+61KeSjamuwyrVZCRQiBLu7K0kegeGYW5syPohHtqNxoXQB72aVSIlM4d+yw8RdPC6rsMRQgihRxLTs/n4x1N0X3yAe6mZLO7dhOUDmkkCWAwkCRSiBLtwJ5k+y8IwVatYP8QDZ2vpNIV+6VSvAlNer4uTlQkzfz1PRnaurkMSQgihYxqNhq3Hb+I9dx/rwm4woOVL7PqgDa82qCgzRoqJTAcVooSKiEmmd+BBDFUK1g/xoHJ5SQCF/jFQKXm7VVWq2ZkxYMVhQi/f5eXasr+rEEKUVdfiUvlk62n2X4qjobMlKwc2o74eVMIuayQJFKIEuhybgl9AGAqFgnVDPHjJ1kzXIQnxnzyqlcdUreK3c9GSBAohRBmUmZPL0n1XWLAnArVKyadv1qOPRxVUShn50wVJAoUoYa7FpeIfcJC8PA0bhnpQ3U42TH1ROTk5fPTRR0RGRpKTk8P48eNxc3Nj+PDhJCYmYmCQ31VOmDCB+vXrExgYSEhICAqFgnfeeYc2bdqQnJzM2LFjSU5OxtTUlLlz52JlZaXbG9MjxoYqvOs48POJW3z8Wh1M1fLrRwghyorQy3f56MdTXIlN5bWGFZnyel0pYqdj8ltYiBIk8l4a/gEHycrJY8PQFtRwMNd1SKXC1q1bMTExYd26dVy6dIlJkyaxadMmUlNTWbp0KRYWDzdIj4yMZPv27WzYsIGUlBR8fX1p1aoVq1atonnz5gwePJi1a9cSEBDAuHHjdHhX+qevRxV+OnGL+b9HML5jLVn3IYQQpdzdlEymbz/H5vCbVLIxYeXAZrStJbNB9IEkgUKUEFHxafh+d5DUrFzWDXGnVgVJAAvLm2++yeuvvw6AjY0NCQkJAKSmpj5ybFhYGF5eXqjVamxsbHByciIiIoLQ0FC+/PJLANq3b8+IESOKLf6SotlL1rzesCKL916mvJmawV7VdB2SEEKIIpCXp+H7I5HM+PU8aVk5jHq5Ou+8XAMTtUrXoYn7JAkUogS4nZiOf0AYSRnZrBvsQT1HWUBdmAwNDbUfr1q1SpsQpqWl8emnn3L79m1q1qzJpEmTiIuLw8bm4ca1tra2xMbGFmi3tbUlJiameG+iBFAoFMz3a0xaVi5f/3aRfRdjqVPRgsmd6+g6NCGEEIXkwp1kPtpyiiPX42n+kg3Tu9WXmUt6SJJAIfRcdFIGft8dJD41i6DB7jRwlgTwRQQHBxMcHFygbfTo0Xh5ebF27VrOnDnDkiVLABg2bBienp7Y2dkxZcoU1q5di0ajKXCuRqNBoVAUaH/QJh6lUCj4slsDei4NZf+lOPZfiuPDV2qhNpAdi4QQoiRLz8rlm92XCNx/BXNjA2b3aIhPU2f5fainJAkUQo/FJGfgF3CQ2ORMVg9yp1ElK12HVOL5+Pjg4+PzSHtwcDC///47ixYt0o4MduvWTfv19u3bs337dtzd3bl69aq2PTo6Gjs7OxwcHIiNjcXc3FzbJh6vgqUx297xZG3YDebsuMCR6/doWd0WgGV/XuVeaiZDW1fH0sTwCVcSQgihD34/H82UrWeIik/Hp6kzkzrXwcZMreuwxH+QV69C6Km4lEx6B4RxOyGDFQOb07SKta5DKrUiIyPZsGEDCxYswMjICIDc3Fz69+9PSkoKkL8WsEaNGnh4eLB3716ysrKIjo4mJiYGFxcXPD09CQkJAWDnzp14eXnp7H5KAitTNQNavoSJoYpxwScZHnSUljN28/nPZ1m45zI/Hrup6xCFEEI8wZ3EDEasOcrbK49gbKhi41AP5vi4SgJYAshIoBB66F5qFn0Cw4iMT2PFgOY0r2rz5JPEcwsODiYhIYGhQ4dq25YtW0b37t3p168fJiYmODg4MHr0aExMTOjZsyd9+vRBoVAwbdo0lEolffv2Zdy4cfj7+2NhYcGcOXN0eEclg5mRAasHNeerHRc4cj2euJRMzNQqzIwM+Csijv4tX9J1iEIIIR4jN0/DqgPXmLvzAjl5GsZ1rMUQr2oytb8EUWj+ucClhIuKisLb25vdu3fj7Oys63CEeGYJaVn4B4RxOTaFZf2b0aqGra5DEoVA+qYnO30zEWNDFcv+vMLPJ25z+OP2GBtKJTkhipr0T+JZnIxKYPKWU5y+mUTrmnZ83qUeVcqb6Tos8YxkJFAIPZKYnk3fZYeIiEkhoL+bJICiTKnvlF/0qHODiqw/FEnfZWF819cNa5lWJMqw9PR0Jk6cyN27d8nMzGTkyJHUrl2b8ePHk5ubi52dHXPmzEGtVrNt2zZWrVqFUqmkV69e9OjRg+zsbCZOnMitW7dQqVTMmDGDSpUq6fq2RAmUlJHN3B0XWH3wOrbljFjg35jXGlSUwi8llIzZCqEnkjOy6b/8EOfvJLGkbxPa1JTCIqJs8qphx3y/xpyITOStxQe4cTdN1yEJoTN79uyhfv36rFmzhnnz5jFz5ky+/fZb/P39WbduHU5OTmzatIm0tDQWLlzIypUrCQoKIjAwkISEBH7++WcsLCxYv349Q4YMYe7cubq+JVHCaDQafjl5m/Zz97H64HX6eVRh99g2vN7QURLAEkySQCH0QEpmDgNWHOb0zUQW+jehXW0HXYckhE694erI2iHu3EvLotuivzh2I17XIQmhE507d2bIkCEA3L59GwcHB8LCwvD29gbA29ub0NBQTpw4QYMGDTA3N8fY2Bg3NzfCw8MJDQ2lQ4cOALRq1YqjR4/q7F5EyXPjbhoDVhxm1Lpw7C2M2DrKk0+71MfCWKo3l3SSBAqhY2lZOby94jDHIxOY79eYV+pV0HVIQuiFZi/Z8MOIlpgZGeAXcJCQ03d0HZIQOuPr68uHH37I5MmTSU9PR63OnyZtZ2dHbGwscXFx2Ng8LCJma2v7SLtKpUKpVJKVlaWTexAlR1ZOHgv3RNDhf/s4cu0eU16vy48jPWnobKXr0EQhKdY1gampqUyYMIHExESys7MZNWoULi4uMq9dlFnpWbkMWnmEI9fv8Y1vY15tUFHXIQmhV6rblWPzyJYMXnWEEWuP8vFrdRnUqqquwxKi2G3YsIFz584xbty4AlPwHtT3+2edP41Gg0Kh+Nd2If7Noav3+GjLKS7FpPBq/QpMfaMeFSyNdR2WKGTFOhK4ZcsWqlatSlBQEN988w3Tp0+Xee2izMrIzmVo0BEOXr3L1z0b8Yaro65DEkIv2ZYzYv0QD16p68DnP59l2rYz5OaVqsLWQvyr06dPc/v2bQDq1KlDbm4uJiYmZGRkABAdHY29vT0ODg7ExcVpz4uJicHOzg4HBwdiY2MByM7ORqPRYGgoU/nEo+6lZjEu+AQ9l4aSlpXL8gFuLO7TVBLAUqpYk0Bra2sSEhIASEpKwtraWua1izIpMyeXYUFH+TMijjk9XOna2EnXIQmh10zUKhb1bsrbnlVZeeAaI9YcJT0rV9dhCVHkjhw5wvLlywGIi4sjLS2Nli1bsmPHDgB27tyJl5cXrq6unDp1iqSkJFJTUwkPD8fNzQ1PT09CQkKA/CIz7u7uOrsXoZ80Gg3BRyLxnruXLcduMrxNdX77oLXUJyjlinU66GuvvcbmzZvp0KEDSUlJLF26lBEjRrzwvPYH5wtREmTl5DFyTTj7LsYy660G9GgqezIJ8TRUSgVT3qhLJRsTPvv5LL4BB1nW3w3bcka6Dk2IIuPr68tHH32Ev78/GRkZTJkyhfr16zNhwgQ2btyIo6MjXbt2xdDQkLFjxzJo0CAUCgWjRo3C3Nyczp07c+DAAfz8/FCr1cycOVPXtyT0SERMMpO3nObQ1Xs0rWLN9G71qV3BQtdhiWJQrEng1q1bcXR0ZNmyZZw/f56PPvpI5rWLMiU7N4/R68PZfT6GL7rWp1ezyroOSYgSZ6BnVRytTBiz4RjdFv3FyoHNqW5XTtdhCVEkjI2NH7v8ZcWKFY+0derUiU6dOhVoe1BDQYi/y8jOZcHvESz94zKmagNmdm9AT7dKKJXyd3VZUazTQcPDw2nVqhUAtWvXJjo6Wua1izIjJzeP9zYcZ8eZaD59sx59PKroOiQhSqyO9SqwYWgL0rNy6b7oAIeu3tN1SEIIUSLsuxjLK//7gwV7InjD1ZHfx7bBt3llSQDLmGJNAqtUqcKJEycAuHnzJmZmZjKvXZQJuXkaPvj+BL+cus3Hr9Whf8uXdB2SECVeo0pWbB7hSflyavoEhrHtxC1dhySEEHorJimDUevC6b/8EAYqBeuGuPN1z0aUlyn1ZVKxTgft1asXkydPpk+fPuTk5DBt2jSqV68u89pFqZabp2Fc8Am2nbjFxFdrM9irmq5DEqLUqFzelM0jWjI06Cjvrj9GVHwaI9pUl6UCQghxX26ehrVh15kTcoHM3Dw+6FCTYW2qYWSg0nVoQoeKNQk0MzPjm2++eaRd5rWL0iovT8PEH06y+dhNPnylJsPbVNd1SEKUOlamaoIGNWdc8Elmh1wg8l46n3eph4GqWCe7CCGE3jl9M5GPtpziRFQirVxs+bxrfaramuk6LKEHijUJFKIsycvT8NGPpwk+GsUY7xq8066GrkMSotQyMlAxr1cjnK1NWLT3MrcS0lnYuwnljOTXnBCi7EnJzOHrnRdZeeAqNmZGfOPbiDddHWWWhNCS345CFAGNRsPUbWdYf+gGo16uznvtJQEUoqgplQrGd6qNs7Upn2w9Tc8loawY2AwHC9noWAhRNmg0GnacucO0bWeJTs6gt3tlxnWsjaWJFFIUBUkSKEQh02g0fPbzWYIOXmdY62p8+EotefMmRDHyd69MRStj3lkbTteFf7FiYDPZ90oIUepF3ktj2rYz7D4fQ52KFizu04TGla11HZbQU7JgQohCpNFo+HL7OVb8dY23Pasy8dXakgAKoQMv17Ln++EtyNNo8Fkcyp+X4p58khBClEDZuXks2XeZV/73B6FX7vLxa3X46R1PSQDFf5IkUIhCotFomL3jAgH7r9K/RRU+eb2OJIBC6FA9R0u2jPTE0cqEASsOEXwkUtchCSFEoTpy7R6vf/snM389j1cNW377oA2DvapJYSzxRDIdVIhC8r/fLrJ472X83Ssz7c16kgCWIDt27OCrr76iQoUKALRs2ZIRI0Zw/vx5pk2bBkCtWrX49NNPAQgMDCQkJASFQsE777xDmzZtSE5OZuzYsSQnJ2NqasrcuXOxsrLS0R2JBxytTAge0YKRa8IZt+kkkfHpvN++hvx8CiFKtIS0LGb+ep4NhyNxtDQmoJ8bHeo66DosUYJIEihEIfh29yW+/T2CXm6V+KJLffkDs4RJS0ujd+/eDBgwoED79OnTmTx5Mg0bNmTMmDHs27ePatWqsX37djZs2EBKSgq+vr60atWKVatW0bx5cwYPHszatWsJCAhg3LhxurkhUYCFsSErBjZj0uZTfLv7ElHxaczs3hC1gbwpF0KULBqNhi3HbjL9l3MkpGcztHU1xnjXwEwqIYtnJP9ihHhBi/ZG8PVvF+nexIkZ3RugVEoCWNKkpqY+0paVlcXNmzdp2LAhAN7e3oSGhhIbG4uXlxdqtRobGxucnJyIiIggNDSUL7/8EoD27dszYsSIYr0H8d8MVUrm9GhIZRtTvv7tIncSM1jcp6lUzBNClBiXY1P4eMtpQq/cpXFlK4K6NqCuoxS9Es9HkkAhXkDAH1eYHXKBLo0cmdPDVRLAEiotLY19+/bxxx9/oNFomDBhAtbW1lhYPPzlamdnR2xsLFZWVtjY2GjbbW1tiY2NJS4uTttua2tLTExMsd+H+G8KhYJ3vWvgbG3ChB9O4rPkAMsHNMPZ2lTXoQkhxL/KyM5l0d7LLNl7GWNDJdO71cevWWX5m0O8EEkChXhOy/+8yvTt53itYUXm+riiks64RAgODiY4OLhAW/v27Rk9ejQeHh4cOXKEcePGERgYWOAYjUZT4H//3q5QKAq0P2gT+ql7E2cqWBgzbM1Rui06wIoBzajvZKnrsIQQ4hF/Xorj4x9Pce1uGl0bOfLRa3WxMzfSdViiFJAkUIjnEBR6jc9+PkunehWY16uRVOEqQXx8fPDx8fnXr7u5uXHv3j2sra1JSEjQtkdHR2Nvb4+DgwNXr14t0G5nZ4eDgwOxsbGYm5tr24T+auliyw8jWjJwxWF6Lg1lgX9j2tWWogpCCP0Qm5zJF7+cZevxW1S1NWPNIHda1bDVdViiFJG/XIV4RuvCbvDJ1jO0r2PPt36NMZQEsMRbuHAhO3bsAODixYvY2NigVqupVq0aR44cAWDnzp14eXnh4eHB3r17ycrKIjo6mpiYGFxcXPD09CQkJKTAsUK/1XQwZ8vIllSzM2PwqiMEHbyu65CEEGVcXp6GNQev027uXn49dYcx3jX4dYyXJICi0MlIoBDP4PsjkUzecoqXa9mxsHcTqS5YSnTp0oVJkyYRFBRETk4O06dPB2Dy5MlMmTKFvLw8XF1dadmyJQA9e/akT58+KBQKpk2bhlKppG/fvowbNw5/f38sLCyYM2eOLm9JPCV7C2M2Dm3B6PXH+OTH00TdS2NCp9qy1kYIUezO3kriox9PcexGAi2rl+fzrvWpbldO12GJUkqh+ecClxIuKioKb29vdu/ejbOzs67DEaXI5vAoxgafoJWLLQH93DA2VOk6JFGCSN+k33Jy85j20xnWHLyhXecrP+OirJD+SbdSM3OYt+siy/+6hpWJIR+/XoeujZxkbbkoUjISKMRT2Hr8Jh8Gn6BFtfKSAApRChmolHzepT6VrE2Z8et5ohMzCOjnhrWZWtehCcHs2bM5evQoOTk5DBs2jAYNGjB+/Hhyc3Oxs7Njzpw5qNVqtm3bxqpVq1AqlfTq1YsePXqQnZ3NxIkTuXXrFiqVihkzZlCpUiVd35K4b+eZO0zbdoZbiRn4Na/MhE61sDKVfkcUPUkChXiCX07e5oPvT+D2kg2B/SUBFKK0UigUDGtTHSdrEz74/gTdFx9g5cBmVClvpuvQRBl28OBBLl26xMaNG4mPj6dbt260aNECf39/Xn31VWbPns2mTZvo2rUrCxcuZNOmTRgaGtK1a1fat2/Pnj17sLCwYO7cuezbt4+5c+cyb948Xd9WmXczIZ1p287w29loalcwZ75/Y5pWsXnyiUIUElnQJMR/2HHmDmM2HKNxJStWDGiGqVremwhR2r3e0JF1g91JSMui26IDhN+I13VIogxr1qwZ33zzDQCWlpakp6cTFhaGt7c3AN7e3oSGhnLixAkaNGiAubk5xsbGuLm5ER4eTmhoKB06dACgVatWHD16VGf3IvKnngf8cYUOX+/jz0txTHq1Nj+NbiUJoCh2kgQK8S92n4vmnXXhNHC2ZMXAZpgZSQKoT1auXKnrEEQp5vaSDZtHemJubIDfdwcJOX1b1yGJEiAzM5P169ezePFirly5UuBrixYteq5rqlQqTE1Ngfx9Tlu3bk16ejpqdf6UQTs7O2JjY4mLi8PG5mEiYWtr+0i7SqVCqVSSlZX1XLGIFxN+I543FvzF9O3naFGtPL990JphbapLlXGhE/KvTojH2HshhhFrwqlT0YJVbzfH3NhQ1yGJf/j99991HYIo5aramrF5REvqOlowYm04gfuvUMpqqYlCNnbsWCIjIzE0NGTMmDH8+OOP2q8dPHjwha69a9cuNm3axJQpUwoUDHnwb/Kf/zY1Gg0KheJf20XxSUzP5qMtp3hr8QHiU7NY0qcpgf3dcLY21XVoogx7qqGN27dvExsbS8OGDdm6dSunT5/Gz8+PatWqFXV8QhS7/ZdiGRp0lBoO5Qh62x0LSQB1pkWLFo9t12g0pKSkFHM0oiwqX86I9UM8eH/jcb745RxR8el88npdVLKFhHiMxMRExo8fD4C/vz8jR44kLy+P7t27v9ALhP3797NkyRICAwMxNzfHxMSEjIwMjI2NiY6Oxt7eHgcHB/bu3as9JyYmhkaNGuHg4EBsbCy1a9cmOzsbjUaDoaH8XisOGo2GbSdu8fnP57iXmsnbnlV5v0NNysnMIqEHnmokcNy4cRgaGnL8+HF++OEHOnXqpN1H61lt27aNN998k+7du7Nv3z5u375N37598ff3Z8yYMdopCtu2beOtt97Cx8eHTZs2AZCdnc3YsWPx8/OjT58+REZGPlcMQvybA5fjGLzqCNVszVgzyB1LU/lFqUs9evTg3XffJTQ0tMB/Bw8epHHjxroOT5QRxoYqFvo3YXCrqqw8cI1hQUdJy8rRdVhCD+Xl5XH69GkATE1NWbRoET/99BNLliwhJ+f5/s0kJycze/Zsli5dipWVFQAtW7Zkx44dAOzcuRMvLy9cXV05deoUSUlJpKamEh4ejpubG56enoSEhACwZ88e3N3dX/xGxRNdjUul3/JDjNlwHCcrY7a904pPXq8rCaDQG0+VBKpUKurUqcOOHTvo378/TZs2JTc395m/WXx8PAsXLmTdunUsWbKEXbt28e233+Lv78+6detwcnJi06ZNpKWlsXDhQlauXElQUBCBgYEkJCTw888/Y2Fhwfr16xkyZAhz58595hiE+DdhV+4yaOURqpQ3Ze1gdykNrwc++OADkpKSSEtLe+RrtWrV0kFEoqxSKhV8/HpdPn2zHr+fj8bvu4PEJmfqOiyhZz755BNmzZpFamoqkJ8IBgYGotFouHnz5nNdc/v27cTHx/Pee+/Rt29f+vbty/Dhw/nxxx/x9/cnISGBrl27YmxszNixYxk0aBADBw5k1KhRmJub07lzZ/Ly8vDz82Pt2rWMHTu2MG9Z/ENmTi7f7LpEx3l/cPxGAp93qcfmkZ7Ud7LUdWhCFPBUryNyc3NZvHgxv//+O++99x4nT57UdnDPIjQ0lBYtWlCuXDnKlSvH559/Trt27fj000+B/ApXK1eupGrVqtoKV0CBClddu3YF8itcffzxx88cgxCPc/T6PQauPIyjlTFrB3tQvpyRrkMS3C/ZP2zYY79Wr169Yo5GCOjf8iUcrUwYvT6cbov+YuXAZrjYm+s6LKEnateuTVBQUIE2lUrFiBEjqFChwnNds1evXvTq1euR9hUrVjzS1qlTJzp16vTI958xY8ZzfW/xbA5cjuPjLae5EpfKG66OfPJaHewtjHUdlhCP9VRJ4Jw5c9ixYwcLFy7EyMiIqKgobeL2LKKiotBoNLz33nvExMQwevToQqlw9eB8IZ7HsRvx9F9+GAcLY9YP8cDOXBJAfXPq1CkCAgJISEgA8qeGx8XF0a1bN90GJsqkDnUd2Di0BYNWHab7ogN8188Nj2rldR2W0CPSZ5UtcSmZfPnLOTYfu0llG1NWvd2cNjXtdB2WEP/pqaaDWlpaUqlSJU6fPs2PP/5IVlYWFy9efK5vGB0dzVdffcXMmTOZNGmSVLgSOnUyKoF+yw9hY6Zm3RB3eWOnp7744gv8/f1JS0tj/PjxNG/enMmTJ+s6LFGGuVayYstIT+zMjei37BBbjz/fVD9ROkmfVTbk5WlYf+gG3nP38dPJW4xu58LO91tLAihKhKcaCRw4cCDOzs7Y29tr254n+SpfvjyNGzfGwMCAypUrY2ZmhkqlkgpXQidO30yk77JDWJoYsn6oBxUtTXQdkvgXxsbGeHh4oFarqV+/PvXr12fQoEG8/PLLug5NlGGVbEzZPMKToUFHGLPhOFHx6YxsW11eTgrps8qAC3eS+WjLKY5cj8e9qg3Tu9WXqeGiRHmqJNDQ0LBQirC0atWKiRMnMmTIEBISEkhLS6NVq1bs2LGDLl26FKhw9fHHH5OUlIRKpSI8PJzJkyeTkpJCSEgIXl5eUuFKvJDzd5LouywMM7WK9UM8cLKSBFCfmZiYsHv3bpydnfn666+pVKkSt2/L5t1C9yxNDVk9qDnjN51kzo4LRN5L4/Ou9WXz5zJO+qzSKy0rh292X2LZ/quYGxvwlY8rbzVxkpc/osR5qiTw5ZdfZt++fTRt2hSVSqVtNzF5tj+cHRwc6NixI/379yc9PZ2PP/6YBg0aMGHCBDZu3IijoyNdu3bF0NBQW+FKoVAUqHB14MAB/Pz8UKvVzJw589nuVgjgUnQyvQPCMDJQsX6oB5VsZLNWfffVV18RFxfHlClTWLlyJRcuXGD27Nm6DksIAIwMVMzr1YhK1qYs2BPBrcQMFvVuIqXgyzDps0qnjOxcuiz4i0sxKfRyq8TEV2tLJXFRYik0T7F76SuvvPLI/jYKhYLdu3cXWWDPKyoqCm9vb+0bOCH+LiImBd/vDqJUwIahHlSzK6frkMRT+Oyzz5gyZUqBtvfee4958+bpJqDnIH1T2bDh0A0++vE0NR3MWTGgGRUsZZ1xWVTS+izpn/7bn5fi+G7/FczUKn49fYclfZrQqX5FXYclxAt5qteUO3fuBCAxMRGlUqndukGIkuRqXCr+AQcBWDdEEsCSYMeOHaxYsYJLly5x8uRJbXtWVhZ5eXk6jEyIx/NtXpmKViaMXHOUbov+YvmAZtSpaKHrsEQxkT6r9IhJzmDG9vPsPhdNUsbDgZDWNe3oWO/5tvsQQp88VRJ44MABPv30UwwMDMjLy0OpVPLZZ5/RtGnToo5PiEJx424a/gEHyc3TsH6oBy72kgCWBB07dqRdu3bMmDGDQYMGaduVSiV2dlJ9TeinNjXtCB7ekrdXHsZnSSiLejehtVQLLBOkzyodbiWkM27TCY5ci6drIycqlzfl9YYVuZeaRT1HS1n/J0qFp1q5/u233xIUFMQvv/zCr7/+SmBgYKEUihGiOETeS8Mv4CDp2bmsGexOTQcZyS5JDA0NmTRpEseOHSMkJAQnJyeSk5Mf2TJGCH1S19GCLaNa4mxtwtsrD/P94UhdhySKifRZJdv6QzdoOfN3/oq4y7iOtZjVoyGjXnahSnkzGle2Rm0gRZ9E6fDU1UH/vj1ExYoVMTCQBe9C/91KSMc/8CDJGdmsG+Ih07JKqClTpmBjY8OhQ4cYNGgQhw4dYsmSJXz99deFcv3Fixdz4MABAPLy8oiLi2PHjh0MHz6cxMREbX83YcIE6tevT2BgICEhISgUCt555x3atGlDcnIyY8eOJTk5GVNTU+bOnYuVlVWhxCdKpoqWJgQPb8HIteGM/+EkkfFpfNChpowilAFF3WeJwpeYns2GQzeYGXKeyjamvFq/An1bVNF1WEIUmafK5Jydnfn0009p3rw5Go2GsLAwKleuXNSxCfFC7iRm4BdwkIS0bNYOdqe+k6WuQxLP6fbt28yYMYO+ffsC0KdPH0JCQgrt+iNGjGDEiBEAbNmyhbt37wKQmprK0qVLsbB4+PIgMjKS7du3s2HDBlJSUvD19aVVq1asWrWK5s2bM3jwYNauXUtAQADjxo0rtBhFyWRubMjyAc34aMsp5v8eQVR8OrPeaiijCaVcUfdZonDFJmfi+10ol2NTaVm9PMv6N8NErXryiUKUYE/1W2j06NGUL1+eo0ePcuzYMRwcHBg1alRRxybEc4tJysA/4CB3U7JY/XZzGjpb6Tok8QKys7NJSkrSjqBcvnyZrKysQv8+OTk5rF+/nj59+gD5SeA/hYWF4eXlhVqtxsbGBicnJyIiIggNDaVDhw4AtG/fntDQ0EKPT5RMhiols95qyNgONdly7Cb9loeRmJat67BEESquPku8GI1GQ16ehrk7LxB5L501g9xZO9hdEkBRJjzVSOBHH32Ej48PnTt3BmDv3r1MnjyZFStWFGlwQjyP2ORM/APDuJOUweq3m9O4srWuQxIv6P3332fAgAFcu3aNjh07olQqmT59eqF/n507d9KqVSuMjfPL+qelpfHpp59y+/ZtatasyaRJk4iLi8PGxkZ7jq2tLbGxsQXabW1tiYmJKfT4RMmlUCgY7V0DZxsTxm86yVtLDrBiQDPZp7SUKq4+Szw/jUZDn2VhnIxMJDkzhwEtX6JVDVtdhyVEsXmqkcCMjAxtAgjQtm3bR/YNFEIf3EvNok9gGDfj01kxoBluL9k8+SSh965fv87du3epUKECKpWKzMxMbt269VzXCg4OpmfPngX+279/PwA//PAD3bt31x47bNgwJkyYwNq1a8nNzWXt2rWPFHfQaDQoFIoC7Q/ahPinbo2dWf22OzFJGXRbdICTUQm6DkkUgcLss0TR+PH4Tf6KuItNOTXD2lTjg1dq6jokIYrVU40EOjo6MmvWLJo0aUJeXh4HDx7E0dGxqGMT4pkkpGXROzCMa3dTWTGgGe7Vyus6JFFIVq9ezdatW7WFVu7du8fAgQN5/fXXn/laPj4++Pj4PNKelpZGdHR0gY2Su3Xrpv24ffv2bN++HXd3d65evaptj46Oxs7ODgcHB2JjYzE3N9e2CfE4LaqX54cRLRmw4jC9lh5kgX9jvOs46DosUYgKs88ShS8mKYMvt5/H1dmSLSM9USrlpZ0oe55qJHDWrFlUq1aNAwcOcPjwYVxdXfniiy+KOjYhnlpiWjZ9loVxOTaFwP5utHSRKR2lSZUqVQoUZ7G2ti704lTnz5+natWq2s9zc3Pp378/KSkpQP5awBo1auDh4cHevXvJysoiOjqamJgYXFxc8PT01BZ+2LlzJ15eXoUanyhdajiYs2VUS1zsyzFk9RGCQq/pOiRRiIqjzxLP5/rdVF7+ai/xqVl82qW+JICizHqqkUADA4PHvjkXQh8kZWTTb3kYF++ksLRfU7xqyAhMaTFr1izttMquXbvStGlTFAoFx48fL5CwFYbY2NgCa/1UKhXdu3enX79+mJiY4ODgwOjRozExMaFnz5706dMHhULBtGnTUCqV9O3bl3HjxuHv74+FhQVz5swp1PhE6WNvbszGYR68u/4Yn2w9Q2R8OhM71ZY/Skuw4uyzxPNZ8dc1snLz+Gl0K9k2SpRpstmfKNFSMnMYsPwQZ28nsbh3U16uZf/kk0SJUbNm/hqNGjVqFGhv0KBBoX+vjh070rFjxwJtXbp0oUuXLo8c27dvX23p9wfMzMxYtGhRocclSjdTtQFL+7rx6U9n+O6PK0TFp/F1z0YYG0p1wpKoOPss8ezSsnIIPhLJGw0dJQEUZZ4kgaLESs3MYeCKQ5yISmShfxPa15U1NaXN39fkCVFaqZQKPn2zHpWsTZm+/RzRSWEE9HPDxkyt69DEMyqqPuvixYuMHDmSAQMG0KdPH27fvs348ePJzc3Fzs6OOXPmoFar2bZtG6tWrUKpVNKrVy969OhBdnY2EydO5NatW6hUKmbMmEGlSpWKJE59t+PMHVKzcunVrGzevxB/J7vVihIpPSuXQasOE34jgW99G9OpfgVdhySEEM9NoVAwpHU1FvVuwqmbiXRf9BfX4h7dp1KUPWlpaXz++ee0aNFC2/btt9/i7+/PunXrcHJyYtOmTaSlpbFw4UJWrlxJUFAQgYGBJCQk8PPPP2NhYcH69esZMmQIc+fO1eHd6E5Gdi7L/7yGk5UJzaRyuBCSBIqSJyM7l8GrD3Po6j2+7unKaw0r6jokIYQoFJ0bVGT9EHcS07Pptugvjl6/p+uQhI6p1WoCAgKwt3+43CEsLAxvb28AvL29CQ0N5cSJEzRo0ABzc3OMjY1xc3MjPDyc0NBQOnToAECrVq04evSoTu6jKGg0GsZvOkHjz3ay98J/78366U9nOXUzkY9fqyPrboVAkkBRwmRk5zJk9REOXL7LVz6udGnkpOuQhBCiUDWtYsPmkZ5YmhjiFxDG9lO3dR2S0CEDAwOMjY0LtKWnp6NW508XtrOzIzY2lri4uALFrWxtbR9pV6lUKJVKsrKyiu8GitClmBS+PxJFfFo2n/18lpm/nmfUunCu/mMUPeT0HdYfusHwNtV5tYG8OBYCJAkUJUhmTi4j1hxl/6U4ZnVvSPcmzk8+SQghSqCqtmZsHulJfUcLRq0LJ+CPK2g0Gl2HJfTEgwqkgPbfxT//fWg0GhQKxb+2l3Shl+/yyv/+AGD2Ww25EpvKkn2X+eXkbT74/rj2vo/diGfi5pM0cLLkgw6yIbwQD0hhGFEiZOXkMWrtMfZciOXLbg3oKYu6hRClnI2ZmnVDPPjg++NM336OqPg0prxRD5VMZSvzTExMyMjIwNjYmOjoaOzt7XFwcGDv3r3aY2JiYmjUqBEODg7ExsZSu3ZtsrOz0Wg0GBoa6i74QqDRaJi27QwArVxs8/8mUEDYlXs0qmzFJz+eZt6uS2Tk5LLiz2vYWxgx368xagMZ+xDiAflpEHovOzePd9cfY9e5aD7rUg9/d9lwVwhRNhgbqljg14ShrauxKvQ6w4KOkpaVo+uwhI61bNmSHTt2ALBz5068vLxwdXXl1KlTJCUlkZqaSnh4OG5ubnh6ehISEgLAnj17cHd312XoLyw6KYN2c/dxITqZaW/UJbC/GwA93Soxt6crfs0q4epsyTe7L7F03xXKl1OzeWRLXrI103HkQugXnYwEZmRk8NprrzFq1ChatGghZY7Fv8rJzeO9jccJOXOHKa/XpV+Ll3QdkhBCFCulUsHkznWoZG3C1G1n8P3uIIH93bA3N37yyaLEO336NLNmzeLmzZsYGBiwY8cOvvrqKyZOnMjGjRtxdHSka9euGBoaMnbsWAYNGoRCoWDUqFGYm5vTuXNnDhw4gJ+fH2q1mpkzZ+r6ll7IVzsucDUulT4elfFzr4yRQcE9NQ1UShb2bsKagzfoUNeeSjam8rMixGMoNDpYZPC///2PP//8k969e3P48GFat27Nq6++yuzZs3F2dqZr165069aNTZs2YWhoSNeuXdmwYQN79uzh5MmTTJ06lX379rFlyxbmzZtX4NpRUVF4e3uze/dunJ1lzVhJlpun4YPvj7P1+C0md67N0NbVdR2SEM9N+iZRGHadjWb0+mOUL6dm5cBmuNib6zokUQqUhP5Jo9GwZN8VZoWcZ1jrakzqXEfXIQlRohX7dNDLly8TERFB27ZtASlzLB4vL0/D+E0n2Xr8FuM61pIEUAghgPZ1Hdg4zIOM7Dy6LzrAwSt3dR2SEEXuRGQCTb/YxayQ87zWsCLjOtbSdUhClHjFngTOmjWLiRMnaj+XMsfin/LyNEzafIofwqN4v31NRr3souuQhBBCbzR0tmLLyJbYWxjTd1kYPx67qeuQhChSP4RHcS81izk9GrLArzEGKilpIcSLKtY1gT/++CONGjUqsI5PyhyLv9NoNHyy9TQbj0Qyup0LY9rX0HVIQgihdyrZmPLD8JYMW3OE9zYeJyo+jVEvu8jvRFGqzN15gbSsXHadjaZDXQd83KQOhBCFpViTwL179xIZGcnevXu5c+cOarW6zJc5Fg89KPm8Nix/Q1fZz0cIIf6dpakhq95uzsQfTvHVzotExafzedf6GMooiSgFTt9MZP7vEdrPx3eqrcNohCh9ijUJ/HsRl/nz5+Pk5MSxY8fYsWMHXbp0KVDm+OOPPyYpKQmVSkV4eDiTJ08mJSWFkJAQvLy8SkWZY/GQRqPhi1/OsSr0OoNbVWVCp1ryRlsIIZ7AyEDF1z1dcbY2Yf7vEdxKzGChf2PMjeUFqSi5rsalsjbsBiaGKtrVtqdxZSvedHXUdVhClCo63yx+9OjRTJgwoUyWORb5NBoNM0POs+zPqwxo+RIfvVZHEkAhhHhKCoWCsa/UopK1KZO3nMJnSSgrBjajoqWJrkMT4plF3kvj5a/2AuBd256FvZvoNiAhSimdJYGjR4/WfrxixYpHvt6pUyc6depUoO3B3oCi9NBoNMzdeZGl+67Qx6MyU9+oKwmgEEI8h57NKlHB0piRa8PptvAAKwY2o05FC12HJcQziYhJ0X7sXs3mP44UQrwIWTggdOqb3ZdYsCcC32aV+OzN+pIACiHEC2hd047g4S0A8FkSyh8XY3UckRDP5kpcqvbjFtVsdRiJEKWbJIFCZxbuiWDerkv0aOrMl90aoFRKAiiEEC+qTkULfhzlSSUbUwauPMz3hyN1HZIQT+1aXCrmRgZsf9eLBs6Wug5HiFJLkkChE0v3XWbOjgt0a+zErLcaSgIois2hQ4do0aIFe/bs0badP38eX19ffH19mTp1qrY9MDCQHj164OPjw759+wBITk5m6NCh+Pn5MWjQIBISEgA4cOAAPXr0oFevXixcuLBY70mIf6pgacz3wzzwdLFl/A8n+WrHhUe2WRJCH12NS6WanRl1HWUqsxBFSZJAUewC919hxq/necPVkTk9GqKSBFAUkxs3brBixQqaNm1aoH369OlMnjyZDRs2kJCQwL59+4iMjGT79u2sW7eOpUuXMn36dHJzc1m1ahXNmzdn/fr1tGvXjoCAAAC++OIL5s+fz/r169m/fz8RERGPC0GIYmNubMiy/m74NqvEgj0RvL/xOJk5uboOS4j/dDUulaq2ZroOQ4hST5JAUaxWHbjGF7+c49X6FfhfT1cMZD8rUYzs7OxYsGAB5cqV07ZlZWVx8+ZNGjZsCIC3tzehoaGEhYXh5eWFWq3GxsYGJycnIiIiCA0NpUOHDgC0b9+e0NBQIiMjsbS0pGLFiiiVStq0aUNoaKhO7lGIvzNUKZnRvQHjOtbix+O36L/8EIlp2boOS4jHysjO5VZiOi9JEihEkZO/wEWxWRt2nanbztChrgPf+jWWBFAUOxMTE1QqVYG2+Ph4LCweTjuys7MjNjaWuLg4bGweVqaztbV9pN3W1paYmBhiY2Mfe6wQ+kChUDDqZRe+8W1E+PUE3lpygMh7aboOS5RgqZk5rDpwjby8wp1ifONeGhoNMhIoRDHQ+T6BomzYePgGH205Tbva9izwb4yhJICiiAUHBxMcHFygbfTo0Xh5ef3neQ/WTf1z/ZRGo0GhUBRof1zbA1LpVuibLo2ccLAwZujqI3RbdIDlA9xo6Gyl67BECTRmw3F2nYumgbMlTSpbF9p1r96vDCpJoBBFT5JAUeQ2HY1i4uZTtK5px6LeTTAyUD35JCFekI+PDz4+Pk88zsbGRlvcBSA6Ohp7e3scHBy4evVqgXY7OzscHByIjY3F3Ny8QFtcXNwjxwqhbzyqlWfzyJYMWHGYXksPMt+vMe3rOug6LFHC7DoXDUBh1xp6kATKdFAhip4Mx4gitfX4TcZtOoFndVu+69sUY0NJAIV+MTQ0pFq1ahw5cgSAnTt34uXlhYeHB3v37iUrK4vo6GhiYmJwcXHB09OTkJCQAsc6OzuTkpJCVFQUOTk57NmzB09PT13elhD/ysXenC0jPanhUI6hQUdYHXpN1yGJEiQmKUP7cWEXGroWl4ptOTUWxoaFel0hxKNkJFAUmZ9P3uL9jcdxr2pDQD83SQCFzu3du5dly5Zx5coVzpw5Q1BQEMuXL2fy5MlMmTKFvLw8XF1dadmyJQA9e/akT58+KBQKpk2bhlKppG/fvowbNw5/f38sLCyYM2cOANOmTWPs2LEAdO7cmapVq+rsPoV4EjtzIzYM9eDd9ceZsvUMkffSmPRqHdmuRzxRSmaO9uOsnLxCu+5PJ26x9fgtmlW1efLBQogXJkmgKBIhp28zZsNx3KrYsKx/M0zUkgAK3Wvbti1t27Z9pN3FxYV169Y90t63b1/69u1boM3MzIxFixY9cmyzZs3YuHFjocUqRFEzVRuwtG9TPv/5LAH7r3IzIZ2vezaSF3biP2XlPkz8CisJPBARx+j1x3CrYs1XPRoWyjWFEP9NkkBR6H47G807647RqJIVywc2w8xI/pkJIYQ+UikVTH2jLs7WJkzffo47iQcJ6OdG+XJGug5NFLIvv/ySEydOoFAomDx5snZbnGeVmf0w8csspCTw9/MxqA2UrBnsLi8hhCgmsiZQFKo952MYufYo9ZwsWTmwGeUkARRCCL2mUCgY7FWNRf5NOHMribcWH9AW6BClw6FDh7h+/TobN27kiy++4PPPP3/uaxXFSODha/doVMlKEkAhipEkgaLQ/HExlmFrjlK7ggWr326OuSzsFkKIEuPVBhVZN8SDpIwcui/6i6PX7+k6JFFIQkNDad++PZA//T0pKYmUlJTnutbfRwL/nhA+r9TMHE7fSqL5S7IWUIjiJEmgKBR/RcQxZPURXOzKETSoOZYmkgAKIURJ07SKNZtHtMTKVI1fQBi/nLyt65BEIYiLi8Pa+uF+fuXLlyc2Nva5rpWV+7AiaGb2i1cHPXc7idw8DY0qWb3wtYQQT0+SQPHCDl65y6BVh6lqa8aawe5Ymap1HZIQQojn9JKtGT+MaElDJ0tGrQvnuz8uoynsDeFEsfrn/38ajQaF4vkqwRb2SOC5O8kA1HG0eOFrCSGeniSB4oUcvnaPt1ceppK1KWsGu2NjJgmgEEKUdDZmatYMdue1BhX5cvt5pmw9Q04h/MEvdMPBwYG4uDjt5zExMdja2j7XtQp7TeD520lYGBvgaGn8wtcSQjw9SQLFcwu/Ec+A5YeoYGnM2iHu2Eo1OSGEKDWMDVXM92vMsNbVCDp4nWFBR0nLynnyiULveHp6smPHDgDOnj2Lvb095cqVe65rFRgJfMEk8PTNRNaG3aB2RYvnHpkUQjwfKd0onsuJyAT6LzuEnbkR64d4YG8ub/CEEKK0USoVTOpcB2cbU6ZuPU2vpQdZNsBN+vwSpkmTJtSrVw9fX18UCgVTp0597mtl5hbeFhEfbTkFQOPKVi90HSHEs5MkUDyz0zcT6bssDCszQ9YN8cDBQv4YEEKI0qyvRxWcrIx5Z90xui08wMqBzajhYK7rsMQz+PDDDwvlOn8vBvMiSWB2bh7nbifTsZ4DH3SoWRihCSGeQbFPB509eza9evXirbfeYufOndy+fZu+ffvi7+/PmDFjyMrKAmDbtm289dZb+Pj4sGnTJgCys7MZO3Ysfn5+9OnTh8jIyOIOv8w7eyuJPsvCMDc2ZP0QDxytTHQdkhBCiGLQrrYDG4e2ICs3j+6LD3DgctyTTxKlzoPEz9zI4IUKw1yMTiYrN4/ODSpiZCD7AwpR3Io1CTx48CCXLl1i48aNBAYG8uWXX/Ltt9/i7+/PunXrcHJyYtOmTaSlpbFw4UJWrlxJUFAQgYGBJCQk8PPPP2NhYcH69esZMmQIc+fOLc7wy7wLd5LpsywME0MV64d44GxtquuQhBBCFKMGzpZsGdmSChbG9F9+iC3HonQdkihmD9YBljM2KLA+8FmduZkEQAMny0KJSwjxbIo1CWzWrBnffPMNAJaWlqSnpxMWFoa3tzcA3t7ehIaGcuLECRo0aIC5uTnGxsa4ubkRHh5OaGgoHTp0AKBVq1YcPXq0OMMv0yJikukdeBBDlYL1QzyoXF4SQCGEKIucrU3ZNKIlblVseH/jCb7dfUm2kChDMnPyUKuUGBuqXmgk8EJ0MsaGSl4qb1aI0QkhnlaxJoEqlQpT0/zkITg4mNatW5Oeno5anb+tgJ2dHbGxscTFxWFjY6M9z9bW9pF2lUqFUqnUTh8VRedKbAp+AWEoFArWDfHgJVvpsIUQoiyzNDFk1dvN6d7Yia9/u8iEH06SLVtIlAlZOXmoDZSoVUqycp5/s/jbiek4WpmgVEpVUCF0QSdbROzatYtNmzYxZcqUAiWBH7xJ/LdNTQtzs1PxdK7fTcU/IIy8PA3rBrtT3e75SkoLIYQoXdQGSub2dOVd7xp8fySKt1ceJjkjW9dhiSKWmZOLkYESI0PlCxWGuZWQQUXZG1AInSn2JHD//v0sWbKEgIAAzM3NMTExISMjA4Do6Gjs7e0fu6mpnZ0dDg4OxMbGAvlFYjQaDYaGhsV9C2VG5L00/L47SGZOLuuGeEglOCGEEAUoFAo+6FCT2T0aEnr5Lj5LQrmdmK7rsEQRKjgS+PxJ4J3EDCpaSnE5IXSlWJPA5ORkZs+ezdKlS7GysgKgZcuW2g1Md+7ciZeXF66urpw6dYqkpCRSU1MJDw/Hzc0NT09PQkJCANizZw/u7u7FGX6ZEhWfhu93B0nNymXNYHdqVZAEUAghxOP1dKvEyoHNuRmfTteFf3HmVqKuQxJFJDMnDyMDJWqD508C07JyiEmWkUAhdKlY9wncvn078fHxvPfee9q2mTNn8vHHH7Nx40YcHR3p2rUrhoaGjB07lkGDBqFQKBg1ahTm5uZ07tyZAwcO4Ofnh1qtZubMmcUZfplxOzEd/4AwkjKyWTfYg3qOUrlLCCHEf2tVw5bgES0YuOIwPZeEsqhPU9rUtNN1WKKQPRgJNDJQkpKZ88zn305Mp8WM3wFkJFAIHSrWJLBXr1706tXrkfYVK1Y80tapUyc6depUoE2lUjFjxowii09AdFIGft8dJD41i6DB7jRwlgRQlC6HDh1izJgxfPnll7z88ssAnD9/ns8++wylUomFhQVz584lIyODTp06UbNm/ibG1tbWfPvttyQnJzN27FiSk5MxNTVl7ty5WFlZceDAAb7++mtUKhWtW7dm1KhRurxNIXSidgULfhzlycAVh3l75WGmd62Pb/PKug5LFKL8NYEq1AbK59oiYt+FWO3HMhIohO7opDCM0E8xyRn4BRwkNjmTlW83p1ElK12HJEShunHjBitWrKBp06YF2r/44gsmTpzImjVrqFKlCps3byYtLY2mTZsSFBREUFAQ3377LQCrVq2iefPmrF+/nnbt2hEQEKC9xvz581m/fj379+8nIiKi2O9PCH3gYGHM98Nb0MrFlombTzFnx3nZQqIUycq9vybQ4Pm2iPgz4mHNB9luSgjdkSRQABCXkknvgDBuJ2SwYmBzmlax1nVIQhQ6Ozs7FixYQLlyBavcLlmyhIYNGwJgY2NDQkICqampj73G3/crbd++PaGhoURGRmJpaUnFihVRKpW0adOG0NDQor0ZIfRYOSMDlvV3w695ZRbuucx7G4+T+QLbCQj9kZmdvybQ6DnXBIZdvUe3xk7sH/+yVBwXQockCRTcS82iT2AYkfFpLB/QjOZVbZ58khAlkImJCSqV6pH2B0lhWloaW7dupVOnTqSlpXHlyhVGjBiBr68v27dvByiwX6mtrS0xMTHExsY+dm9TIcoyA5WSL7vVZ3ynWmw9fou+yw6RkCZ7+5Z0WbkPC8M8a2KfmZNLbHIm1WzNqGQjo4BC6FKxrgkU+ichLT8BvBqXyrL+zWhRvbyuQxKiUAQHBxMcHFygbfTo0Xh5eT32+LS0NEaMGMHbb79N9erViY6OZtSoUbz22mvEx8fTs2dP3NzcCkxr+7c9TAHZw1QI8n8ORrZ1wcnKhHHBJ3lr8QFWDmwuCUAJlpn9cIuIp90nMCUzh/VhN3ilngOQP2VYCKFbkgSWYYnp2fRddoiImBQC+rvRqoatrkMSotD4+Pjg4+PzVMfm5OQwcuRIXn/9dbp37w6Ag4MDb7zxBgDly5enfv36XLlyRbtfqbm5OdHR0do9TP++t+mDdiFEvi6NnKhgYczQoKN0W/QXy/o3w1XWnZdI+SOBKowMn3466OK9ESzcc5mrd/On2dtbGBVliEKIpyDTQcuw8ZtOcP5OEkv6NpEy3qJMCwgIoHnz5gWSxv379/PVV18B+aOE58+fp2rVqgX2K32wt6mzszMpKSlERUWRk5PDnj178PT01Mm9CKGv3KuV54cRLTFRq+j1XSg7z9zRdUjiOWRm56I2UGJjqiYzJ4+Y5IwnnqNS5v+5eeB+URgZCRRC92QksIw4cyuRg1fusfdCDNXtyjHx1dr8FXGXnm6VaFfbQdfhCVEs9u7dy7Jly7hy5QpnzpwhKCiI5cuXs3btWpydnbXFXNzd3Rk6dCg//vgjvr6+5OTkMHToUBwcHOjbty/jxo3D398fCwsL5syZA8C0adMYO3YsAJ07d6Zq1ao6u08h9JWLfTk2j/Bk8OojDFtzlKmv12WAp/yslCRGhipszNS0rJ4/e+jPS3F0b+L83+cY5CeB1+6mAZIECqEPJAksA45HJtBzaah22sb+S3EYKBWkZObQwEn2ARRlR9u2bWnbtu0j7X/++edjj587d+4jbWZmZixatOiR9mbNmrFx48YXjlGI0s7O3IgNQzx4d8Mxpv10lsj4dD7qXAelUtbRlgSr326Opakh5dQGlDdT88fF2CcmgUnp2dqP1Sol1qaGRR2mEOIJZDpoGRBy+g4ajYYlfZqw7R1PqtmaEfjnVQDqOUoSKIQQoniZqFUs6dOUAS1fYtmfVxm5NpyMbNlCoiSoZGOKhbEhSqUCt5esOXkz8YnnJGU8TAKzcvOkcJYQekCSwDLgZFQCtStY0Kl+RRo6W9Gh3sPpnzUcZI8eIYQQxU+lVDDtzXpMeb0uO87ewS/gIHdTMnUdlt45dOgQLVq0YM+ePdq28+fP4+vri6+vL1OnTtW2BwYG0qNHD3x8fNi3bx8AycnJDB06FD8/PwYNGkRCQkKhxfaSrRlR99LJzXu0QvLfJaXnYFsuvxiM7EMshH6QJLAUOx6ZgH/AQQ5cvktD54cjfv1bvEQvt0p80KEmxoaP7pkmhBBCFJe3W1Vlce+mnL2VRPfFB7gSm6LrkPTGjRs3WLFiBU2bNi3QPn36dCZPnsyGDRtISEhg3759REZGsn37dtatW8fSpUuZPn06ubm5rFq1iubNm7N+/XratWtHQEBAocVXxcaMrNw87iT9d3GYpIxsnK1NOP1pR1YMbFZo318I8fwkCSylsnPzGBZ0hAOX7wIUWPvnaGXCrB4Nede7hq7CE0IIIbQ61a/AhqEepGTk0H3xAY5cu6frkPSCnZ0dCxYsoFy5h7N2srKyuHnzJg0bNgTA29ub0NBQwsLC8PLyQq1WY2Njg5OTExEREYSGhtKhQwcA2rdvry2AVRiqlM/f7/H6/a0fALot+otFeyMKHJeUno2FiSHljAywMJb1gELoA0kCS6nvj0QSnZTJ4t5N+KxLPd5wddR1SEIIIcS/alzZms0jW2JjqsY/MIyfT97SdUg6Z2JigkpVcMZOfHw8FhYW2s/t7OyIjY0lLi4OGxsbbbutre0j7ba2tsTExBRafJVtHiSB+VU/8/I0HLuRwOyQCwWOS8rIwcJYahEKoU/kJ7IU2nU2mo+2nKZuRQteqVcBlVRcE0IIUQJUKW/GDyNaMjToCO+sO0ZUfDrDWlcrE4VEgoODCQ4OLtA2evRovLy8/vM8jUZT4H//3q5QKAq0P2grLI5WJhiqFNokMOFvVUD/7sFIoBBCf0gSWArtPh+NubEBm0e2lARQCCFEiWJtpiZokDsfBp9g5q/nibyXxqdv1sNAVbonL/n4+ODj4/PE42xsbAoUd4mOjsbe3h4HBweuXr1aoN3Ozg4HBwdiY2MxNzfXthUWlVKBlamaxPQsAOL+VtgnMycXIwMVGo2GpIxsLCUJFEKvlO4etYw6fC0etyrWUvRFCCFEiWRsqOJb38YMb1OdtWE3GBp0lNTMHF2HpRcMDQ2pVq0aR44cAWDnzp14eXnh4eHB3r17ycrKIjo6mpiYGFxcXPD09CQkJKTAsYXJyEBJZnb+PsRxyQ+TwAejgxnZeWTnamQtoBB6RkYCS5l7qVlExKTQvYmTrkMRQgghnptSqWDiq7WpZGPCJz+eptd3oSzv3wx7C2Ndh1Zs9u7dy7Jly7hy5QpnzpwhKCiI5cuXM3nyZKZMmUJeXh6urq60bNkSgJ49e9KnTx8UCgXTpk1DqVTSt29fxo0bh7+/PxYWFsyZM6dQYzQyUJKZk58Exv5tJPByTAo1Hcy1ewRamMifnELokzL/E7n8z6tsOHyD2T1caVTJStfhcCU2BUcrk+cexXtQUa3ZSzZPOFIIIYTQf73dq+BoacKodeF0W3SAFQObUdPBXNdhFYu2bdvStm3bR9pdXFxYt27dI+19+/alb9++BdrMzMxYtGhRUYWI2kClTQLjUrK07Q+2jUi6v05QRgKF0C9lejpoREwKn/18lovRKWwOj9J1OKw5eJ12c/cxZPUR8p6w8eq/OXI9HrVKWWBLCCGEEKIke7m2Pd8Pa0F2bh5vLT7AgYg4XYck7ssfCcwF8tcEqpQKlAq4ez8hfDgSKEmgEPqkTCeB68JuoFYpca1kxb6LsboOh+/+uALA/ktx7Dhz57mucfjaPRo6W8p6QCGEEKVKfSdLtozypKKlMf1XHOKHo7p/eSsKTgeNS87ErpwRNmZq7qbeTwLT89dyyhYRQuiXEpkEfvnll/Tq1QtfX19Onjz53Nep62jBpM616dHUmet30wjcf6UQo3w2SRnZ3LiXxgcdamJuZMAfl579Lee91CxORSXSrKpMBRVCCFH6OFmZsGlES5pXtWFs8Am+2XXpka0RRPEyMlSRpZ0OmomtuZryZkbcS81fHygjgULopxL3WubQoUNcv36djRs3EhERwaRJkx7ZV+dp9WjqDEBGdi6/n4vmy+3n6NHUGStTdWGG/FTO3koCoIGzJe7VynPg8rMlgfGpWYzfdIKcPA1dGsnG8EIIIUonC2NDVgxozqTNp/jfrotExqfxZbcGqA1K5HvtEk+tejgSeC8tGxszI7Jz8h5OB5U1gULopRLXY4aGhtK+fXsgf2F0UlISKSkpL3RNY0MV77SrQZ4G/oq4WxhhPrMz95PAeo4WeLqU5/rdNG4npj/1+TN+PceuczE0qmRF7QoWRRWmEEIIoXNqAyVf+TTkvfY12HQ0irdXHtaOOIniZWT4cE1gfGoWNqaG2JRTc+/BdNCM/Omg5jIdVAi9UuKSwLi4OKytrbWfly9fntjYF1/P5+psibmxAX/8bW3gncSMZ0rEXsSe8zE4WZlgb25MQ+f8oi4PRgefxoHLd3GyMuG7fk2LKkQhhBBCbygUCt5rX5OvfFw5eOUuPotDuZVQPL+zxUNGBkrtdND4tCysTNXYFlgTmI2RgVJqFQihZ0pcEvjPuf8ajQaFQvHC1zVQKWn2kg3HIuO11x2w4hADVxwu8vUGETEp/BkRh797ZQBt6evzd5L/87zkjGwu3Enmxt00ouLTGdSqKvbmZWf/JCGEEKJHU2dWvd2cWwnpdF34F6dvJuo6pDLlQWGY7Nw8kjNysDFTY2NmRGJ6Ntm5eSRlZMt6QCH0UIlLAh0cHIiLe7heLiYmBltb20K5dk0Hc67GpZKdm0f4jXjO30nW/leU9l6IAR6uUTQ3NqSSjQnnbv/3SODgVUfoOO8P+i0PA8C9mhSEEUIIUfZ4utiyaURLDJQKei0NZc/936ui6BkZqMjMziUhLX86rvX96aCQPz00KT1HKoMKoYdKXBLo6enJjh07ADh79iz29vaUK1euUK5d06Ec2bkart9NY23YDczUKlRKBb+cvF0o1wfIzMlldsh5biemo9FouBaXysmoRGzLGeFg8XAUr3YFi/9MAjNzcjl2IwGAa3fTqF3BnLoVZS2gEE9y6NAhWrRowZ49e7Rtw4cPx8/PT7vR8unTpwEIDAykR48e+Pj4sG/fPgCSk5MZOnQofn5+DBo0iISEBAAOHDhAjx496NWrFwsXLiz2+xKirKtVwZwtozx5ydaMwauOsC7shq5DKhMejATGp+VP/7Q2U1PeLD8JvJuaJSOBQuipEvdqpkmTJtSrVw9fX18UCgVTp04ttGvXsM+fhnn42j1+OXmbHk2dORmVqJ0iWhh2n4th0d7LbDtxi6q2Zuy/vxWEV42Co5n1HS3ZdS46v/N8TEWt87eTycrNY2Tb6iz94wqj29UolGmxQpRmN27cYMWKFTRtWnDtbGpqKkuXLsXC4uGLlMjISLZv386GDRtISUnB19eXVq1asWrVKpo3b87gwYNZu3YtAQEBjBs3ji+++IJly5bh4OCAv78/HTt2xMXFpbhvUYgyzcHCmO+HteCddeFM3nKKyPg0xr1SC6VSfj8WFSMDJVm5edpCMNamagzuP++7KVkkpWfrpOq6EOK/lbgkEODDDz8skutWtzcDYNLmUygV0Nu9Chqu89OJWy+89nDHmTscvnqPmOT8fXOi4tOJin+4gL2qrVmB491eskajgWM3EmhT0+6R652ISgCgj0cVhrauJh2sEE/Bzs6OBQsW8NFHHxVoT01NfeTYsLAwvLy8UKvV2NjY4OTkREREBKGhoXz55ZcAtG/fnhEjRhAZGYmlpSUVK1YEoE2bNoSGhkoSKIQOmBkZENDPjSnbzrB472Wi4tP5yqchRgZSmKQoqA2UaDRo/76xNlWjNrifBKZmkpSRQ+XyZv91CSGEDpTIJLComKoNGNamGrFJmfi5V6auowUNnCxZF3aD63fTeMn2+Tqxq3GpDAs6qv28l1slWte0IyImhZoO5RixNpzKNqYFzmlUyQqVUsGRa/ceSQI1Gg3bjt/CycqEipbGMgIoxFMyMTF5bHtaWhqffvopt2/fpmbNmkyaNIm4uDhsbB6us7W1tSU2NrZAu62tLTExMcTGxj5ybGRkZNHejBDiXxmolEzvWp/KNqbM/PU80YkZfNevqbwwLQIPkus796upW5sZatvupmRxLzVL1gQKoYfkp/IfJr1ap8DnDZzyt2s4eTPxuZLAlX9d5c9/7D34bvsaOFnl/zGq0WgIGtQcj2rlCxxjZmRAnYrmhN8oOBU1JjmDV/73Bwlp2XzRtb4kgEL8i+DgYIKDgwu0jR49Gi8vr0eOHTZsGJ6entjZ2TFlyhTWrl37r5WI/97+uLYH5GdTCN1SKBQMb1MdJysTxn5/gu6LD7ByQHMqlzd98sniqRkZ5peXuJP4t5FAlRKlAk5GJZCYnk3tCua6DFEI8RiSBD5BDYdyqJQKLt5JBldITMum97KDzOjWkAb39/P7N8kZ2Uz76SwADZ0t+a6vG0oF2P+tAIxCocCrxqPTPQHqVLB4pMLZ3guxJKRl83rDivRqVukF706I0svHxwcfH5+nOrZbt27aj9u3b8/27dtxd3fn6tWr2vbo6Gjs7OxwcHAgNjYWc3PzAm1/r1r8oF0IoXtvuDpSwdKYIauP0G3RXywb0IxGlax0HVapoVblJ4F/XIrFycpEux+gjZma385GA9CkivW/ni+E0I0SVx20uBkZqKhqa6bdJuLwtXucvpnEr6efXDH071tLVLU1o4KlcYEE8ElqVTAnLiWLuJRMbdvBy3cpb6Zmvl9jDFXyf58QLyo3N5f+/fuTkpIC5K8FrFGjBh4eHuzdu5esrCyio6OJiYnBxcUFT09PQkJCANi5cydeXl44OzuTkpJCVFQUOTk57NmzB09PT13elhDib5q9ZMMPI1piZmSA73eh7DhzR9chlRoPRgIjYlLo6fbw5bSNmZrUrFzM1CpqOchIoBD6RkYCn0KtCuacisrffPbk/U1oj17/94qh1++mUtnGlDP3j7UzN2KIV7Vn/r61K+RXKrx4JxlbFyM0Gg0HLt/Fo3p5mWomxHPYu3cvy5Yt48qVK5w5c4agoCCWL19O9+7d6devHyYmJjg4ODB69GhMTEzo2bMnffr0QaFQMG3aNJRKJX379mXcuHH4+/tjYWHBnDlzAJg2bRpjx44FoHPnzlStWlWXtyqE+IfqduXYPLIlg1cdYfiao3zyWl3ebiU/py/q7wV3OjeooP24vJkRkEKTKtYYyEtrIfSOJIFPobaDOb+cvM2Px25y+n5idzIqkezcPO1o3Pk7SZy7nUR5MyP6LT/ErLcacPZ2EuXN1Bya7P1cSVvNCvn7H16ITqaliy3X7qZxJymDFv9YPyiEeDpt27albdu2j7R36dKFLl26PNL+YN/AvzMzM2PRokWPHNusWTM2btxYaLEKIQqfbTkj1g/x4L2Nx/js57NExqfx8Wt1UckWEs/NyOBhguds/XC9ZVZuHvDoFlhCCP0gSeBTaOlSnrm/wXsbj6NWKbE0MSQxPZs952MIu3oPT5fyvL3yCAAm9+fCbz91h4iYFFwrWT33qJ1dOSOsTA25FJM/TS30cn6BmRbVJQkUQgghnoeJWsWi3k2Z/ss5lv91lVsJ6czr1RgTtWwh8TzUf0sC//4Mr8blb73T+jHbXAkhdE/G559C0yo2BA9vAeS/2RrWJn9q59Cgoyz78yqDVh3RHpuenYuFsQH7LsZyMyGdro2dnvv7KhQKqtmacSU2Pwk8cDkOBwsjqj3nVhVCCCGEAJVSwZQ36jL1jbrsPBuNX8DBAuvvxdP7t/0X5/Z05Q1XR1kPKISekiTwKTX+WyWx/i1eKvA1jQZq2Jfjsy71+Pi1OqwZ7I6pWoW9uRGv1HV4oe9b1bYcV+NSScrIZve5GF6uZS/rAYUQQohCMNCzKkv6NOX8nSS6LzrA5fsvXfVFTk4OEyZMwN/fn549e3LkSP5L5/Pnz+Pr64uvry9Tp07VHh8YGEiPHj3w8fFh3759ACQnJzN06FD8/PwYNGgQCQkJhRrj36eD/t3LteyZ79dY/mYRQk9JEviUDFRKPnylJu+3r4mZkQHljPJn0k7oVBuAtrXs6NfiJQZ7VaOhsxXHpnRgz4dttaWSn1c1OzOikzJp/NlvpGfn0sejygvfixBCCCHydaxXgQ1DW5CWlcNbiw9w6Oo9XYektXXrVkxMTFi3bh3Tp09n5syZAEyfPp3JkyezYcMGEhIS2LdvH5GRkWzfvp1169axdOlSpk+fTm5uLqtWraJ58+asX7+edu3aERAQUKgxPpgOam1qWKjXFUIULUkCn8E77Wowpn0NAFYPas6ETrXp7VGZ1jXt8HEruGefkYEKM6MXX3L5YOpnbp6GAS1for7Tf+9NKIQQQohn06iSFZtHeGJjpqZPYBi/nHzyNlDF4c0332TSpEkA2NjYkJCQQFZWFjdv3qRhw4YAeHt7ExoaSlhYGF5eXqjVamxsbHByciIiIoLQ0FA6dOgA5O+DGhoaWiSxWpupi+S6QoiiIUngc2pS2ZoRbatjYWzI6rebU7OI5rzXdczfJmJy59pMe7NekXwPIYQQoqyrXN6UzSNa0qiyFd/uvqTrcAAwNDTEyMgIgFWrVvH6668THx+PhYWF9hg7OztiY2OJi4vDxsZG225ra/tIu62tLTExMYUao8395M+3WaUnHCmE0CdSHVTPVSlvxvEpHbAylTdsQgghRFGyMlWzcagHaVm5xf69g4ODCQ4OLtA2evRovLy8WLt2LWfOnGHJkiXcu1dwuqpGoynwv39vVygUBdoftBUmBwtjTk57BfNCmP0khCg+8hNbAkgCKIQQQhQPhUJRKMs5npWPjw8+Pj6PtAcHB/P777+zaNEiDA0NtdNCH4iOjsbe3h4HBweuXr1aoN3Ozg4HBwdiY2MxNzfXthU2C2NZDyhESSPTQYUQQggh9FBkZCQbNmxgwYIF2mmhhoaGVKtWTVspdOfOnXh5eeHh4cHevXvJysoiOjqamJgYXFxc8PT0JCQkpMCxQgghI4FCCCGEEHooODiYhIQEhg4dqm1btmwZkydPZsqUKeTl5eHq6krLli0B6NmzJ3369EGhUDBt2jSUSiV9+/Zl3Lhx+Pv7Y2FhwZw5c3R1O0IIPaLQ/HMSeQkXFRWFt7c3u3fvxtnZWdfhCCEEIH2TEEJ/Sf8kRNkj00GFEEIIIYQQogyRJFAIIYQQQgghyhBJAoUQQgghhBCiDCl1hWFyc/P39rlz546OIxFCFLYKFSpgYFAyuy3pm4QovUpy3wTSPwlRmv1b/1Rye6x/ERsbC0Dv3r11HIkQorCV5KIF0jcJUXqV5L4JpH8SojT7t/6p1FUHzcjI4PTp09jZ2aFSqXQdjhCiEJXkt+3SNwlRepXkvgmkfxKiNPu3/qnUJYFCCCGEEEIIIf6dFIYRQgghhBBCiDKk5M5d0LHZs2dz9OhRcnJyGDZsGA0aNGD8+PHk5uZiZ2fHnDlzUKvVtGrViqpVq2rPW7lyJXl5eUycOJFbt26hUqmYMWMGlSpVKtL4XnnlFYKCgpg5cyaHDh3CzMwMgG3btrFq1SqUSiW9evWiR48eZGdn6018+vL8GjRowKRJk8jJycHAwIA5c+ZgZ2enN8/v3+LTl+dnZ2fH7NmzMTAwQK1WM2fOHGxsbHT2/Eo76Z+KJz59eX7SP71YfNI/FR/pm4onPn15ftI3vVh8Rd43acQzCw0N1QwePFij0Wg09+7d07Rp00YzceJEzfbt2zUajUYza9Yszdq1azV5eXmabt26PXL+5s2bNdOmTdNoNBrN3r17NWPGjCny+LZs2aL5+uuvNW3bttWkpKRoNBqNJjU1VfPKK69okpKSNOnp6ZqOHTtq4uPj9SY+fXp+48eP1/zyyy8ajUajWbNmjWbWrFl69fweF58+Pb/Ro0drbty4odFoNJr58+drFi9erLPnV9pJ/1Q88enT85P+6cXik/6peEjfVDzx6dPzk77pxeIr6r5JpoM+h2bNmvHNN98AYGlpSXp6OmFhYXh7ewPg7e1NaGgoaWlp2rLLfxcaGkqHDh2A/Lc1R48eLfL4vL29ef/991EoFNrjTpw4QYMGDTA3N8fY2Bg3NzfCw8P1Jj59en5Tp06lY8eOAFhbW5OQkKBXz+9x8enT8/vf//5HpUqV0Gg0REdHU6FCBZ09v9JO+qfiiU+fnp/0Ty8Wn/RPxUP6puKJT5+en/RNLxZfUfdNkgQ+B5VKhampKQDBwcG0bt2a9PR01Go1AHZ2dsTGxpKWlsbdu3d599138fX1ZfXq1QDExcVhY2OjvZZSqSQrK6tI4zM3N3/kuL/HAWBra0tsbKzexKdPz8/U1BSVSkVubi7r1q3jjTfe0Kvn97j49On5qVQq/vjjDzp16kRcXBxvvvmmzp5faSf9U/HEp0/PT/qnF4tP+qfiIX1T8cSnT89P+qYXi6+o+yZZE/gCdu3axaZNm1i+fLn2TQKA5n7BVRMTE8aMGUOXLl3Izs6mT58+NGnSRPv1vx//97c4RRHf4/xbHPoSn749v9zcXMaPH4+HhwctWrRg27Ztj41DX+JLSUnRq+fXunVrvLy8+Oqrr/juu+9wdHR8bBzFFV9pJ/1T0canb89P+qcXi0/6p+IjfVPRxqdvz0/6pheLryj7JhkJfE779+9nyZIlBAQEYG5ujomJCRkZGQBER0djb29PuXLl8PHxQa1WY2ZmRosWLbhw4QIODg7ajVmzs7PRaDQYGhoWaXyP4+DgQFxcnPbzmJgY7Ozs9CY+fXt+kyZNokqVKrzzzjuA/j2/f8anT8/vt99+A0ChUNCxY0eOHj2q0+dX2kn/VPTx6dvzk/7p+eOT/qn4SN9U9PHp2/OTvun54yvqvkmSwOeQnJzM7NmzWbp0KVZWVgC0bNmSHTt2ALBz5068vLy4cOECEyZMQKPRkJOTQ3h4ODVq1MDT05OQkBAA9uzZg7u7e5HH9ziurq6cOnWKpKQkUlNTCQ8Px83NTW/i06fnt23bNgwNDXn33Xe1x+nT83tcfPr0/ObPn8+5c+eA/PUUVatW1dnzK+2kfyqe+PTp+Un/9GLxSf9UPKRvKp749On5Sd/0YvEVdd8k00Gfw/bt24mPj+e9997Tts2cOZOPP/6YjRs34ujoSNeuXTE0NMTKygofHx+USiUvv/wyDRs2pF69ehw4cAA/Pz/UajUzZ84s8vjc3d0JCwsjNjaWIUOG0KhRI8aPH8/YsWMZNGgQCoWCUaNGYW5uTufOnfUmPn15frdu3cLCwoK+ffsCUL16daZNm6Y3z+/f4tOX5/fJJ5/w6aefolKpMDY2Zvbs2RgbG+vk+ZV20j8VX3z68vykf3qx+KR/Kh7SNxVffPry/KRverH4irpvUmj+OYlUCCGEEEIIIUSpJdNBhRBCCCGEEKIMkSRQCCGEEEIIIcoQSQKFEEIIIYQQogyRJFAIIYQQQgghyhBJAoUQQgghhBCiDJEkUAghhBBCCCHKEEkChRBCCCGEEKIMkc3ihV7y8fFh7ty5VK5cmTt37jB8+HDq1q1LZGQkOTk5vPvuu7Ro0YIDBw7wzTffYGhoiIWFBfPmzePYsWMsX76ctLQ0JkyYQP369XV9O0KIUkT6JyGEPpK+STwLGQkUeqlLly5s374dgN27d9OhQwfs7OwICgpi4cKFfPnllwAkJiby1VdfsWbNGsqVK8eff/4JwMWLF1m2bJl0YkKIQif9kxBCH0nfJJ6FjAQKvfTaa68xaNAghg8fzt69e7G1teXUqVOEh4cDkJmZSVZWFjY2Nnz88cfk5uYSGRmJh4cHZmZm1KpVC7VareO7EEKURtI/CSH0kfRN4llIEij0krW1NRUqVODkyZPk5eVhZmbG8OHDef311wscN3nyZL777juqV6/OZ599pm2XTkwIUVSkfxJC6CPpm8SzkOmgQm916dKFzz77jE6dOuHq6squXbsAuHv3Ll9//TUAKSkpVKxYkaSkJMLCwsjOztZlyEKIMkL6JyGEPpK+STwtSQKF3nr55Ze5ceMGr7zyCq+++ipmZmb4+voyfPhwmjZtCoC/vz9+fn588sknDB48mKVLlxIbG6vjyIUQpZ30T0IIfSR9k3haCo1Go9F1EEI8zsGDB9myZQuzZs3SdShCCFGA9E9CCH0kfZN4WrImUOilb7/9lj///JP58+frOhQhhChA+ichhD6Svkk8CxkJFEIIIYQQQogyRNYECiGEEEIIIUQZIkmgEEIIIYQQQpQhkgQKIYQQQgghRBkiSaAQQgghhBBClCGSBAohhBBCCCFEGSJJoBBCCCGEEEKUIf8H0x8m6sHNHZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if generate_plots and scc_output == 'point-est':\n",
    "    fig, axes = plt.subplots(1, len(ds.coeff.values), figsize=(15, 4))\n",
    "\n",
    "    for i, coeff in enumerate(ds.coeff.values):\n",
    "        lines = coeffs_all_years.sel(variable=ssp,age_adjustment='vsl', vsl_value='epa', heterogeneity='scaled')[coeff].plot.line(\n",
    "            x='year', ax=axes[i], add_legend=False)\n",
    "        \n",
    "        axes[i].set_title(coeff)\n",
    "\n",
    "    sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-73fe97d63bf6>:53: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  axes[0, -1].legend(\n"
     ]
    },
    {
     "data": {
      "image/png": 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nJ/3BPRxd13E4HNjtdi699FIURQFg+fLlZGZmpkvQJOnr9E3Fkx49epCbm8uGDRsA0ndBDy6x/v73v8+SJUvQdR2wYs/gwYMPmyS22rlzJwMGDGDp0qXccMMN6bG/9NJLVFVV8eabb/J///d/bNiwgTfeeCP9c3v27OGf//wn8+bN49FHH6WhoYFPPvmEv/71r/zjH/9g2bJlJJNJ/vKXvxx17H/605/wer2sWLGCn/3sZ22qCI6kZ8+eeL1errvuOt544w1qamrQNI38/HwAzjnnHPbv38/Pf/5zli9fTjAYxOVykZWVdcRztsaT1p/v0qULADU1NbzzzjtceOGFxxyXJH1Vp0I8+cMf/sDVV1/Nm2++ySuvvMLatWtJJpPH/RqOdL3xn//5n2RmZrJ06VL+9re/8fe//z39/JFIBEVReOONN5g5cyYzZsxA13XeeuutdOz517/+xaeffsrixYsZMWIE+/fvp6ysDICysjKqq6sZPnw4s2bN4txzz+Wtt95i5MiRrF279phjPlbMGDFiBO+++y733nsv77zzDpFIBK/Xi9frPeI5U6lUOqacf/755OXlAbB79262bNnCeeedd9zvqdT+li9fzsKFC3nwwQfT149glQ0DBAIB7rnnHm655RamT59Ojx490iXFt912G88//zy33nor99xzzwk/t0wUT3Fvv/02/fv3p1evXgBcc801vP322xiGAcBFF11EVlYWqqoyduxYPvzwQwBee+01Jk6cCMC5556bDlDHcs4555CdnU1WVhZ5eXnpO0i9evWipqbmhMd/uPGVl5dTV1fHpZdeCkD//v0pKipiy5YtAJxxxhkMGjQIsMojWl/TjBkzeOCBBwDo1KkTeXl5lJeXM3DgQIQQbNu2DbB+OU2cOJE9e/aQSCTSr+G6667DNM1jjvnCCy/kv//7v6muruaWW25h6NChTJ8+naamJgCuvfZaHn30UT799FOuv/56hg0bxqOPPkoikTjs+crKyliyZAnjxo1LH/vwww8ZNWoUDz30EI8++uhRk0xJai/fZDy5+OKL0zdw1qxZw4ABA9okga0zcuvWrQOsX4SXXHLJUc+ZkZGRHsfFF1/M1q1bicVirFq1iquuugqbzYbL5eIHP/gB7733XvrnrrjiCgC6d+9Ot27d2LJlC/369WPVqlV4vV5UVeXss89Ov64jjX3Dhg18//vfB6y4NWDAgGO+D263m1deeYUBAwbw+9//npEjR/KjH/2I999/H4CCggIWLFhAfn4+Dz/8MMOHD+eGG25Ix7MvSiaTvPDCC23iCVhxaezYsYwdO/aY1SOS1B5OhXiSk5PD0qVL+fTTT8nKyuK//uu/Tuj36ZGuN1avXp2etQ8EAowbNy4dU4QQXHnllYCVlOm6zt69exk/fjz/+Mc/sNvtOJ1O+vfvT1lZGQ6HgwsvvDA9/uXLlzN27FjsdnubmDJu3Lj0DaSjOVbMOOuss/j73/+OaZrce++9DB06lFtvvZXKysrDnq+5uZm//e1vbWKKYRiMGzeOyy+/nJ/+9Kf07NnzuN/Tb6tsr50eBRnt+t/RZhNbrVmzhmeffZbnnnsOn8+H2+0mHo8DUF1dnf5/ZuLEibzyyis888wzCCHo2LEjAwYMYNiwYQAMHjyYhoaG9OfzeMlE8RQXDofZvHkzEyZMYMKECVx11VV4vd70mraDyxX9fj+hUAiARYsWceWVVzJ+/Hj+3//7f+m7DsfSWm4Jbae8VVU9riTriw43voaGBnw+X5u7In6/n4aGBsCqwz7ca9qyZQs33ngjF198MRMmTKC2tjY9pnHjxrFixQqi0SibNm1izJgxNDU1tSkHPZ4A3GrEiBE888wzvP/++zz33HPs3LmThx56KP39iRMnMm/ePD744APmzJnDypUr+cMf/pD+/uzZs5kwYQLjx4/nV7/6FdOnT29zUXn22Wfzzjvv8N///d/88pe/POJFoSS1p28ynhw803+kJPD73/8+b7zxBvF4nPfff5+xY8ce9Zx+vz8dN/x+P0A6phwcNzIzM6mvr2/z9cF/D4VCxGIxfvvb3zJ+/HjGjx/P3/72t/TrOtLYQ6FQ+nmBdOOIYykoKGD69OksW7aMFStWMHjwYG666ab0+96tWzd+85vfsHr1at544w0KCgr493//93R8++ijj9L/ZpMmTcLr9TJt2rQ2z/HXv/6VtWvX8vnnn/O73/3uuMYlSV/FqRBP7r77bnr16sWdd97J6NGj+etf/3pCr+FI1xsNDQ1tPut+vz8dUxRFOeTnmpqaaGho4N5772X8+PFMmDCBFStWpF/b+PHj2zWmHCtm9O/fn9mzZ7N27VpeeeUVkskkv/zlL9M/v3Tp0vS/2zXXXMOgQYPaNDzRNI233nqLt99+m0WLFvH3v//9uMYlta9wOMysWbP405/+lP48jRgxgqVLlwKwbNkyRo4cia7rXHfddSQSCWpra9m6dSv9+vVj7ty56ceWlpaSnZ2NpmknNAa5RvEUl5+fz4gRI/j9739/2O83Njam/97U1ERmZibV1dXMmDGDBQsWcOaZZ7Jnz56T1lXzcOPLycmhqakJIUT6oi8YDJKTk0NlZWWbxi6tPwNWg5jrr7+ea665BkVRGDlyZPpx48eP59FHH6Vnz54MGTIkXWZx8ELeurq64xrz6tWrOeecc/D5fGiaxpAhQ7jlllt48sknSaVSrF69mgsvvBBN03A4HIwePZp9+/a1mcG45557uOyyyw45dzAY5J133uGHP/whAGeeeSaDBg1i/fr1J1w3Lkkn6puMJ3369EHTNLZt28a7777Lfffdd8hjLr30Uq666ipGjRrFOeec0+bi63C+GBvAuhjNzc1t871gMJju6Nf6ujp27Jj+XmZmJi+99BJ79uzh1VdfxePx8OSTT1JdXX3UsXs8nnSJPJBeG3I0u3fvJhqN0rdvXwCKi4u59957efXVVykvL6eyshKXy0X37t0Ba4bjgQce4Nxzz02/xkGDBvHiiy8e9vzLly/nrLPOoqioCK/Xy+WXX87TTz/9pUqMJOlEnArxxOPx8Ktf/Ypf/epXfPzxx/zsZz87oRn1I11vtMaUoqKi9OMO7hLa2NiYLvVs/bknn3wSm83GokWLcDgc3HXXXelzjxo1iv/4j/9gz5497Nmzh6FDh6bH/8WYciyfffbZUWPGrl276NSpEwUFBSiKQr9+/bj77ru5+uqr0+cYP378Edctvvbaa1x00UX4/X6ys7O59NJLWbNmTXptt/TNWbx4MY2NjW26zj7++OPMmDGD+fPnU1RUxKRJk7DZbEyYMIHJkyfjcrl49NFHsdlsXHbZZdx33328/PLL6Lp+1LWqRyJnFE9x5513Hhs2bEiXZnz88cf89re/TX9/zZo1hEIhDMNg+fLl6anljIwMunXrhq7rzJ8/H6BNMPoqbDYb4XD4uB57uPEVFxdTWFjI4sWLAdi0aRN1dXXpGbfdu3fz2WefAdZdr3PPPReA+vp6+vbti6Io/N///R+xWCydCJ5zzjnU19fz6quvpktaunbtimmalJSUAPD3v/+9zSzmkbz88svMnj07XUqaSCRYunQpgwcPxmaz8eSTT/Lss8+mp++bm5t5++23GTx48HG9dw8//HC63K6+vp7NmzenF45L0tfpm44nF198Mc888wxnnnnmYdfcde/enc6dOzNnzpz05/Zo4vE4y5cvB6zY0K9fP5xOJ6NHj2bhwoXpJlv//Oc/2zReaF1LuGvXLvbu3cvAgQOpr6+nW7dueDweKioqWLVqVZsbS4cb+4ABA1i2bBkAW7du5eOPPz7mmLdu3crtt9/eprxu1apVaJpG9+7d02uJWm9kCSFYtGgRPXr0OOo6xVYrVqzgmWeewTRNhBCsWrVKxhPpG3EqxJOf//zn7NixA7CWyLSWkh+vI11vjB49Oj22hoYGli1bxgUXXJD+udY10O+++y4ul4tu3bpRX19Pz549cTgcbNu2jQ8//DAdUxwOB+eddx6zZ89mzJgx6aY8B8eUlStXHtcSn2PFjEWLFvHrX/86/Z7qus6bb77JkCFDjus9efXVV9M3plKpFO+++66MKSfJ5MmTeffdd3n55ZfT/3Xs2JEXXniBv/3tb/zud79Lb4V07bXX8tprr/HKK69w9tlnA9aNyZdffpm//OUv6SUQJ0rOKJ7iCgoKePjhh7n11ltJpVJ4PJ42bdOHDRvGL37xC/bt28eAAQO44oorcDqdjBo1iosuuogOHTowffp0Nm3axJQpU3j99de5/vrrmTZtWvoO94k677zzeOGFF7jiiiv4xz/+cdTzHW58iqLwn//5n/z617/mD3/4A263m6effjpd5nr22Wfz4osvsmnTJjIzM3nqqacAuOOOO7jpppvIy8vj6quvZvLkydx3330sWLCAzp07M3bsWBYsWMCcOXMAKzDPnDmT++67D5/Px09+8hNUVU0nixMmTOAvf/lLm5kHgDlz5jB79mx+8IMfoCgKhmFw0UUXceedd6IoCs899xyzZs1i4sSJ6XP98Ic/5Cc/+ckx37vWPYxmz55NNBrFNE2mTp3K8OHDv9S/hSSdiG86nkyYMIF/+7d/a3Px+EWXXnopTz/9dLoT8F/+8hfq6uoOu29Xx44d2bhxI7Nnz0bTNB5//HEAfvzjH1NeXp5uFDVhwoQ2iWd2djaXXXYZoVCIGTNmkJmZydVXX81tt93GRRddRL9+/bjvvvu49dZbeeGFF/jJT35y2LHffPPN3HHHHYwbN45BgwYxZsyYdAyYM2cORUVFh9x1v+SSSwiHw9x6660kEgkMw6BLly7MmzePjIwMfvazn2GaJj/+8Y8xDANd1+nbty/PPvvscfyLwr333stvfvMbJk6ciBCCHj168Jvf/Oa4flaSvopTIZ5MnTqVu+66i1TK6kY5ZcqUdHOnVkf6bMKRrzd++ctfMnPmTCZMmICqqtx0000MGDCA8vJyNE0jlUpx6aWXkkgk+O1vf4uqqvy///f/mDZtGgsXLmTo0KHce++9TJ8+nYEDBzJx4kQmTJjAbbfd1qY64J577uGuu+7izTffZNSoUQwaNCgdU6ZNm8aECRO46KKL2oz5WDHjP/7jP3jyySfTa7N1Xed73/sejz322HH9uz722GPp124YBueccw4/+9nPjutnpW8hIZ227r33XjF37twT/rn/+q//Etu2bWu3cRzpfF9mfP/4xz/E9ddf304jaysSiYhevXqJUCgkhBDigQceEOFw+Gt5Lkk63ZysePLmm2+K22+/Pf11VVWVeOyxxw553Pr168XYsWNP+Py9evUS+/fv/9Lj+yLTNNN/v+2228SLL74ohBBiw4YN4n/+53/a7Xkk6XR2qlyfCHHkz+aXud4oKysTZ555ZjuNzHJwTPm3f/s38dZbbwkhhPjnP/8pVqxY0a7PJUknSpaefgcVFxenu5SdiudrT1dccUW6xHXx4sWcccYZ6QY3gwcPPmq7aEmSju2rfP5jsRjz5s3juuuuSx+rra3lRz/6UXsNr1395S9/4eabb8Y0Terr63n//ffTJT7JZJIJEyac5BFK0unt67ieOJU/m0888US6Ud6uXbv4/PPP6devHwAul0t2MJZOuq9Uejpr1iw2btyIruvcdNNNlJSU8OGHH6Y7Z954441ccMEFvP7667z00kuoqsrkyZO58sorSaVSTJ8+ncrKSjRN47HHHqNTp07pjdgBevfunf4AzZs3jyVLlqAoCr/4xS++8xt/fhU/+MEPTunztaf77ruP3/zmNzz99NN4PJ50qRqQbigjnRpkPDk9fdnP/8qVK3nooYe44oor2qzvbb1IOhVdfvnlvP/++1x88cXpUrPWNR+yfPzUIuPJ6enruJ44lT+bP/nJT5g2bRrjxo1DVVUefPBBCgsLAWtNpiSddF92KnLdunXipz/9qRBCiIaGBjF69Ggxffp08dlnn7V5XCQSERdffLEIhUIiFouJ8ePHi8bGRvHqq6+KmTNnCiGEWLVqlbjjjjuEEEJMnTpVbN68WQghxO233y5WrVol9u3bJy6//HKRSCREfX29GDdunNB1/csOXZKkU4yMJ5IktRcZTyRJktrHly49HTJkCE8//TRg7UMTi8XS+88cbPPmzfTv3x+fz4fL5WLw4MFs2rSJdevWpTf3PP/889m4cSPJZJKKior0HdoxY8awbt06SkpKGDlyJA6Hg+zsbDp27MjOnTu/7NAlSTrFyHgiSVJ7kfFEkiSpfXzp0tODN2NfsGABo0aNoqGhgT/84Q+EQiEKCgqYMWMGdXV1ZGdnp38uNzeX2traNsc1TUNVVerq6tpsPJqXl0dtbS2BQOCw52ht16vrOlVVVRQWFqZbDkuSdPo4leIJyJgiSaczGU8kSfq2+GIZff/+/Zk2bRqGYZCXl8fs2bNxOBy88sorLFiwALvdzk9+8hPGjx9/xDL6E/GVI9by5ctZuHAhzz//POvXr6dHjx5069aNP/7xjzzzzDMMHDiwzeNFyybrQohDjh/u2MF/fvEcraqqqhgzZgwrVqyguLj4q74kSZJOklMhnoCMKZL0bSDjiSR99zTHDYIxA7dDJTtDQwAVjQliKZPCTAd+lw1TCPbUxuien3Gyh3tU69evZ8eOHcyfP5/GxkYuv/xyhg8fzpQpU5g4cSKzZs1i4cKFjB8/nueff55FixYBcP311zN69Gj+9a9/4ff7mTNnDqtXr2bOnDnpLWCO11fqerpmzRqeffZZnnvuOXw+H+PGjaNbt24AjBs3ju3bt1NQUJDeFBSgpqaGvLw8CgoKqK2tBawNPYUQ5OfnEwwG04+trq4mPz//kHNUV1eTl5f3VYYuSdIpRsYTSZLai4wnkvTdE0lYSaLLrqSTxMpgS5Lob0kSTStJDMeNkz3cYzpcGX1JSUl63+HWEviKigq6d++O0+nE6XTSp08fNm/efNgy+hP1pRPFcDjMrFmz+NOf/kQgEADg5z//OZWVlQCUlJTQs2dPBg4cyJYtWwiFQkQiETZt2sTgwYM577zzWLJkCWB1vxs6dCh2u53u3buzYcMGAJYtW8bIkSMZNmwYq1atIplMUl1dTU1NDT169PiyQ5ck6RQj44kkSe1FxhNJ+u6JJgwaowZOm0KOx5ZOEqNJkwK/A7/bShJ311lJYnG264TOXxtK8ml5uF3/qw0lj/qchyujj8ViOBwO4EAJfOfOnSktLaWhoYFIJMKHH35IfX39Ycvok8mjP+cXfenS08WLF9PY2Midd96ZPnbFFVdw2223kZGRgdvt5rHHHsPlcnHXXXdx4403oigKt956Kz6fj0suuYS1a9dyzTXX4HA40tsW3H///Tz44IOYpsnAgQPTe8hcddVVTJ06FUVRmDlzJqoqt4CUpG8LGU8kSWovMp5I0ndLNGnS0JIk5nqtJHH/QUliptuGYQp218aIJAw6ZbvI9tpP9rCP28Fl9OPHj08fby19DwQC3HPPPdxyyy3k5eXRo0ePI5bMf7E0/lgU8cWznIbKy8tl/b8kSe1GxhRJktqLjCeS9PWJJU3qIzoOTSHXZ0MBKpuSRBIG+T47gQx7mySxc46LLI/9SyVNJ8OaNWt4+umnmTdvHoFAgDFjxvDmm2/icrl4//33+ctf/sLvf//7Nj/zq1/9ih//+Me88sorXHrppYwcOZJUKsVFF13EmjVrTuj55W0vSZIkSZIkSZJOK/GUlSTaD0oS9x8mSfy8JtomSTSFoLQqerKHf0yHK6MfMWIES5cuBQ6UwOu6znXXXUcikaC2tpatW7fSr1+/w5bRnyjZp1mSJEmSJEmSpNNGPGVS12wliXleK51pnUnM+0KSGE2adMl1pY+V7o/QFNNP8is4tsOV0T/++OPMmDGD+fPnU1RUxKRJk7DZbEyYMIHJkyfjcrl49NFHsdlsRyyjPxEyUZQkSZIkSZIk6bSQ0E3qm3VsqrUmEcVakxhJmumZxJRh8nlNjETKpGuui8wMO7phsq0yQnPC4IxTfGsMgMmTJzN58uRDjr/wwguHHLv22mu59tpr2xxr3Tvxq5CJoiRJkiRJkiRJp7yEblIX1tFUyPPZUJQD3U3zfQ4CGTaSupUkJg2Trnlu/G7r2NbKZuJJk16FGWR7HSf7pZwWZKIoSZIkSZIkSdIprW2SaEdRoCKYIHZQd9NEymRXTRTDFHTPc+N12YinDLZWRkjpJn2KPGRmnD4dT082mShKkiRJkiRJknTKOiRJBCoaE8RSJoUt+yTGkwa7amMIAWcUZJDh0IgkdLZVRhACzuzoxeeSqc+JkF1PJUmSJEmSJEk6JSVSbZNEgPJgS5KYaSWJ0aTBzpoYAD3y3WQ4NJqiKT4rb0ZRoG9x2yQxHD/1m9mcCmSiKEmSJEmSJEnSKSfR0t304CSxojFBPGXSIdOB32WjOa6zqzqKpkCP/AxcDo36cJJtlREcdpW+xT7cDi19zrrmJCW7gyfpFZ1e5PyrJEmSJEmSJEmnlHjK6m7amiQKoKIxTkIXFGU68LpshGM6u+tiODSV7vluHDaVqmCCPXUxfC6N3h082LQD82JlDTE+29+Mx6kd+YmlNJkoSpIkSZIkSZJ0yjiQJCrk+WyYQlDRmCBlCDoGnHicGsFIin31cZx2K0m0qQpl9TEqGhNkeWz0LPCgqgoAQghKayLsrouR67UzqNh/kl/h8Zk1axYbN25E13Vuuukm+vfvz7Rp0zAMg7y8PGbPno3D4eDJJ5+kpKQEIQRjx47lZz/7GUuXLuV3v/sdhYWFAIwYMYKbb775hJ5fJoqSJEmSJEmSJJ0SvpgkGqagvDGBKQQds5xkODTqwkkqGhN4nBrd8tyoCuyujVETSpLvd9Atz42iWEmiYQo+rghRHUrSKcvFmR28qC3fO5WtX7+eHTt2MH/+fBobG7n88ssZPnw4U6ZMYeLEicyaNYuFCxcyePBgSkpKeOWVVzBNk0svvZRJkyYRjUa59tprueGGG770GOQaRUmSJEmSJEmSTrpYy5pEm2YlibopKGuMYwpBcZYLt90qLa1oTOB32+ie50YBSqsi1ISSdMxytkkSEymT9/cEqQ4l6V3g4azTJEkEGDJkCE8//TQAmZmZxGIxSkpKGDNmDABjxoxh3bp1+Hw+EokEyWSSRCKBqqq43W4ikchXHoOcUZQkSZIkSZIk6aSKJk0aIjp2TSHXayNpmFQ0JlAUhU7ZThyaQnljgobmFNkeO8XZTnRTsH1/hOa4QddcN4UBZ/p8oZjOprImkrrJoE5+Cv3Oozz70VUE45Q3xtvjZaYVZ7noGHAd8fuappGRkQHAggULGDVqFO+++y4OhwOAvLw8amtr6dChAxMmTODCCy/EMAxuvfVWvF4v0WiU1atX88477yCE4N5776VPnz4nNMavlCgeb93s66+/zksvvYSqqkyePJkrr7ySVCrF9OnTqaysRNM0HnvsMTp16sS2bduYOXMmAL179+ahhx4CYN68eSxZsgRFUfjFL37B6NGjv8rQJUk6xch4IklSe5HxRJJOL5GEQWPUwKEp5PpsxFMmFcEENlWhOMuJpirsrYvTFNPJ9zsozHQQT5ls2x8hqZv0LMwgx+tIn68qlGBLeQi7pjK0W4BMt/0kvrqvZvny5SxcuJDnn3+e8ePHp48LIQAoKyvjrbfeYvny5ei6ztVXX80ll1zCsGHDGDBgAMOGDWPDhg3cc889LFq06MSeXHxJ69atEz/96U+FEEI0NDSI0aNHi+nTp4vFixcLIYR44oknxF//+lcRiUTExRdfLEKhkIjFYmL8+PGisbFRvPrqq2LmzJlCCCFWrVol7rjjDiGEEFOnThWbN28WQghx++23i1WrVol9+/aJyy+/XCQSCVFfXy/GjRsndF1Pj6WsrEz06tVLlJWVfdmXI0nSSXQqxRMhZEyRpNOZjCeSdHoJx3RR1pAQNaGkMExThOMpUVoVEbtroyKlG0I3TLGjKiI+2hsSNU0JIYQQTdGU+GBXUHywKyhCsVT6XKZpip01zeJfn9SItbsaRCypH+lpTwvvvPOOuOKKK0RjY6MQQoiLLrpIxGIxIYQQJSUl4rbbbhNvvvmm+M1vfpP+mV/+8pdi7dq1h5xrxIgRh8SnY/nSaxSPt2528+bN9O/fH5/Ph8vlYvDgwWzatIl169Yxbtw4AM4//3w2btxIMpmkoqKCAQMGtDlHSUkJI0eOxOFwkJ2dTceOHdm5c+eXHbokSacYGU8kSWovMp5I0ukjHDcIxgxcdqvctDluUBlM4rCpdMp2IYCd1VEiCYPOOS7y/A7qwkm2VjRj0xT6dfLic1kFklbTmjA7aqJ0yHTyva4BXPbTdxuMcDjMrFmz+NOf/kQgEACszqVLly4FYNmyZYwcOZLOnTvzySefYJomqVSK0tJSOnXqxNy5c9OPLS0tJTs7G007sffjS5eeHm/dbF1dHdnZ2emfy83NPeS4pmmoqkpdXR1+/4F2ta3nCAQChz1H7969v+zwJUk6hch4IklSe5HxRJJOfUIIQnGDcNzEbVfJ9mgEozq1zSncdpWigJOUIdhdE0U3Bd3y3PhcGhWNccrq4/hcGr06eLC37JEYTxl8WBaiKabTM99D99wDDW1OV4sXL6axsZE777wzfezxxx9nxowZzJ8/n6KiIiZNmoTdbue8885jypQpCCG48sorKS4u5rLLLuO+++7j5ZdfRtd1HnnkkRMew1duZnOsutnWPw8+rijKYY8f7tjRziFJ0reLjCeSJLUXGU8k6dQkhKApZtCcMMlwqATcKnXNKRqjOl6nRmGmg2jCYE9dDAWFM/IzcDvU9PYXOV47Z+RnpPdIbIql2LQvhG6anN3JT8FXaFpzKpk8eTKTJ08+5PgLL7xwyLHbb7+d22+/vc2x4uJiXn755a80hq+0PcaaNWt49tlnee655/D5fLjdbuJxqyNQdXU1+fn5FBQUUFdXl/6Zmpoa8vLyKCgooLa2FoBUKoUQgvz8fILBYPqxRzpHdXU1eXl5X2XokiSdYmQ8kSSpvch4IkmnJiEEwaiVJHqdVpJYHbaSxEy3jQ6ZDkJRnc9rYthUhR6FGTjtKtv2W9tfFGU56VFwIEnc3xSnZHcQRYGh3bKOK0k0heDTqvDX/VK/Fb50oni8dbMDBw5ky5YthEIhIpEImzZtYvDgwZx33nksWbIEgJUrVzJ06FDsdjvdu3dnw4YNbc4xbNgwVq1aRTKZpLq6mpqaGnr06PEVX7okSacKGU8kSWovMp5I0qlJCEF9RCeSNPG5VHxOlcqmJOG4QY7XTp7XRm04yd76OBlOjR4FHhDwaXmYpqhOtzw3nXOsklJTCLZVNbO5PIzfbWN49yz8rmMXSkZTBqt21vPx/tA38IpPf1+69PRE6mbvuusubrzxRhRF4dZbb8Xn83HJJZewdu1arrnmGhwOB48//jgA999/Pw8++CCmaTJw4EBGjBgBwFVXXcXUqVNRFIWZM2eiql9pMlSSpFOIjCeSJLUXGU8k6dRjCkFds05SF2S6NdwOlfLGBAndpMDvwO/SqGhMUN+cIpBho1OOi0jcoLQqgikEfTp4CHisLS6Susnm8hD1kRSdslycWehNzzAeTUVTnJJ9jeim4HudA1/zK/52UMQXC+xPQ+Xl5YwZM4YVK1ZQXFx8socjSdJpTsYUSZLai4wn0nedYVpJYsoQZHs0bKpCRTCBbgg6BJy47Sr76mKE4gZ5PgcdAg7qwik+r4nisKv06eDB7bC6dYZiOh+WNRHXTfp28FKc5T6u599c2cT22ggBl40R3bLJdJ2++yp+k75yMxtJkiRJkiRJkqQv0g1BbXMK04Rcr5V2lDXGEQKKs5zYNIVdNVFiSZOOWU5yvHbK6uNUBhP43TZ6FmakO5tWBuN8UhnGrqkM7RogkHHsZC8c13lvTwONsRQ9cz2c3TET7ThmHyWLTBQlSZIkSZIkSWpXSd2krlkHIM9nI2kI9jcl0BSF4mwnpinYURXFaNn+wuPUKK2K0BjRyfc76JrnRm1Zj7i9OsLe+hhZGXYGdfLjtB27xHt3Q5QNZUFUBUZ2y6Y4cOzZR6ktmShKkiRJkiRJktRu4imT+mYdVYVcr53mhE5tOIXTptIx4CSSMNhXH0NVFc4oyEBTFD4tDxNNmnTNdVOQ6UBRFJK6yUflIRoiKTpnu+lT6EE9xhY0KcNkQ1mQPY0x8rwORnTJJsNxYhvNnypmzZrFxo0b0XWdm266if79+zNt2jQMwyAvL4/Zs2fjcDh48sknKSkpQQjB2LFj+dnPfkYqlWL69OlUVlaiaRqPPfYYnTp1OqHnl4miJEmSJEmSJEntIpo0aIgY2FSFXK9GQzRFMKrjcWoU+u3UN6fYH0zgdqh0y3UTT5lsrWq2mtYUedIlpcFoio/KQyR1k/4dfXQMuI753HWRJOv2NhBJGPQr9NG30HfMxPJUtX79enbs2MH8+fNpbGzk8ssvZ/jw4UyZMoWJEycya9YsFi5cyODBgykpKeGVV17BNE0uvfRSJk2axLvvvovf72fOnDmsXr2aOXPm8NRTT53QGGRrLkmSJEmSJEmSvhIhBKG4lSQ6bFaSWB22ksRAho1Cv52KxgT7gwkyM2z0yM+gIZLis4pmNFWhX7GPQIYdIQR76qOU7AmiAEO7BY6ZJJpCsGV/iOWltQgBF/XMpX8H/2mbJAIMGTKEp59+GoDMzExisRglJSWMGTMGgDFjxrBu3Tp8Ph+JRIJkMkkikUBVVdxuN+vWrWPcuHEAnH/++WzcuPGExyBnFCVJkiRJkiRJ+tKEEASjBpGkiduu4nepVASTJHSTPJ8dn1Njd22cSMKgwO8gz29nT12MmlCSQIaNHgUZ2DQV3TD5pLKZqlCCPJ+DAR196WY2RxKO66zb20B9NEXXLDfndgrgOMbPnKjd9VE+b4i06zm7Z3volpNxxO9rmkZGhvX9BQsWMGrUKN59910cDgcAeXl51NbW0qFDByZMmMCFF16IYRjceuuteL1e6urqyM7OTp9LVVWSyWT654+HTBQlSZIkSZIkSfpSTCFoiOjEUwKfU8VpVyhrTGAIQVHAiU2BHVVRUoagc44Lj1Nja0WE5oRBxywnxdkuFEUhHNf5sCxELGnQq8BDtxw3ylFmBIUQ7KqPsqmiCU2B87pm0/k4tssQQlCyr5FhXbLb82342ixfvpyFCxfy/PPPM378+PTx1h0Oy8rKeOutt1i+fDm6rnP11VdzySWX8MUdEIUQR30/D0cmipIkSZIkSZIknbCD90gMZGgoQHljAkVR6JTlIqmb7KiLoSpW0xrDFGwpC2OYgl6FGWR7rdmtimCcT1u2vhjSNZNsz9FnvWIpg/f3BakMxSn0ORnaOeu4GtbURRL87+YqapqTJ5QodsvJOOrs39dlzZo1PPvss8ybNw+fz4fb7SYej+NyuaiuriY/P58tW7YwcOBA3G4rSe7duzelpaUUFBRQW1tLnz59SKVSCCGw209s/0i5RlGSJEmSJEmSpBOSMkxqwil0Q5Dt0UjpJhXBBHZNoVOWg6Zoit21MRw2lR4FbsJxna0HrUfM9jowTMEnFWG2VIQJZNgZ0T3rmEliRVOMf22roSoc55yOmVxwRs4xk0TDFPxrWw1/XLuPmuYkXbOO3RjnZAuHw8yaNYs//elPBAIBAEaMGMHSpUsBWLZsGSNHjqRz58588sknmKZJKpWitLSUTp06cd5557FkyRIAVq5cydChQ094DHJGUZIkSZIkSZKk4xZPmdRHdBQg16vRGNUJxQ28To08n52KhgRNMauJTceAk731cWrDbdcjRhI6H5WHCMcNuudm0CM/46jNZ1KGyYcVTeyqjxJw2xnTI5dM97FnyHbWRfjnp9WEEwYum8qP+hbQp8Dbju/G12Px4sU0NjZy5513po89/vjjzJgxg/nz51NUVMSkSZOw2+2cd955TJkyBSEEV155JcXFxXTo0IG1a9dyzTXX4HA4ePzxx094DIr4YgHraai8vJwxY8awYsUKiouLT/ZwJEk6zcmYIklSe5HxRPq2aU4YBKPW9heBDI2acJJ4yiTHY8fjUNlTHyeRMikKOPG6NHZWR4l8YT1iRTDOZ/vDqIpC/44+8n3Ooz5nVShOyb4gsZRBn3wv/Tv40dSjr7eLJXVe/aSanXVRAAYW+fj+WfnYVBVTiNO6I+o3Rc4oSpIkSZIkSZJ0VEIImmIGzQkTl00hw6lSGUxgmIIOmQ6EgB3VURRFoXu+m5Qh+KS8GYBeHTxke+zohsln+8NUNiXIyrAzsNiHy37kstGUYfJRZYiddRF8Thtje+WS6zl6Umk1qwny9s56koYg223nqkGFFPqsctN9jTEWbK7irgu6td+b8y31lRLF0tJSbrnlFm644QamTp3Kww8/zIcffojH4wHgxhtv5IILLuD111/npZdeQlVVJk+ezJVXXkkqlWL69OlUVlaiaRqPPfYYnTp1Ytu2bcycOROwFmM+9NBDAMybN48lS5agKAq/+MUvGD169Fd75ZIknVJkPJEkqT3JmCJJ7cc0BfURnYQu8DpVVEVQ0ZhAUxWKs5yEYjpVTUlcdpWuuS6qmpLsDybwODV6Fmbgsms0xVJsLg8RTZr0yMvgjLyMo3bhrA4nKNnXSCR5YBbRdoxZxJrmBAs376cmkkJTYVzPHEZ0zUJRFCJJgzc+q+G93UG8zmM3vpG+QqIYjUZ5+OGHGT58eJtjjzzyCGeeeWabY3PnzmXhwoXY7XYmTZrE2LFjWblyJX6/nzlz5rB69WrmzJnDU089xSOPPML999/PgAEDuOOOO1i9ejXdu3dn8eLFvPLKKzQ3N3P11Vdz/vnno2nyH1mSvg1kPJEkqT3JmCJJ7Uc3BHXNKXQTAm6VuG7SENFx2VUKfA72Bw+sRyzwO9hZHSUcN8j3O+ia60ZRYHddlNKaCE5N5XvH6Gqqt8wi7qiL4HVqjO2ZS5736LOIKcPkX9tq+KgyjCmgW7abHw0oJMNhwxSCdXsaef3TWqJJg9FnZHHBGafH1hgn25fueupwOHjuuefIz89PH4tEDt2IcvPmzfTv3x+fz4fL5WLw4MFs2rSJdevWMW7cOADOP/98Nm7cSDKZpKKiggEDBgAwZswY1q1bR0lJCSNHjsThcJCdnU3Hjh3ZuXPnlx26JEmnGBlPJElqTzKmSFL7iKdMqsMpTAHZGRpNMZ2GiI7fpZHrsbG7NkZTTKco4CTTbePTimYiCYMeBRl0z88gZQo27mtie3WEPK+DEWccvatpTXOCf22rYUddhN55Hib2yT9qkiiE4OPKJp58ZzebKsK4bCrXnN2B6wcXk+Gwsbcxxn+u3sPfP6yiwOfg7gu6EnDZeWDxjq/j7frW+dIzijabDZut7Y9HIhH+8Ic/EAqFKCgoYMaMGdTV1ZGdfSBrz83Npba2ts1xTdNQVZW6ujr8fn/6sXl5edTW1hIIBA57jt69e3/Z4UuSdAqR8USSpPYkY4okfXUHN63xuVSqw0lShiDfZ8c0BTtrYmiKQvc8F00xg101cdx2lTM7eslwaNQ1J9lSESZlmJzVwUunLNcRS01ThsnH+0OU1kbwOjTG9Mwl/xiziLXNCV7dUsX+cBIFGNIpkwm989BUhaZ4ikWf1lKyrwm/U+PH5xbhtCn813v7KA/G6Z3v+RresW+fdm1mc/XVV9OjRw+6devGH//4R5555hkGDhzY5jFCCBRF4YvNVoUQhz128J9fPIckSd9eMp5IktSeZEyRpOMjhCAYNYgkTZw2BYcNKoMJVFWhY8BBQ7NOQySFx6lRFHCwty5OU0wn12unW761Kf3W/c3sbYjhcWoM7pKFz3XklKOyKc4HZUGiKYOeuR4GFfmxaUcuekzoBku21fLx/jCGgI5+J1cOLCTL7SBlmKzYXs+y0np0w2Rszxz6d/Dwj83VbK4Mk+91cPuoLgzplNnu79u3Ubsmiq1lGq1/nzlzJhdffDGrVq1KH6+pqWHQoEEUFBRQW1tLnz59SKVSCCHIz88nGAymH1tdXU1+fj4FBQXs3r27zfG8vLz2HLokSacYGU8kSWpPMqZI0rEZpqC+WSdpCLxOhYRuUh0ycNtVcjx2yhrixFMm+X4HLrvC1soIhinolucm3+8gFNf5uCJMJGHQOdtN7wLPEbexiKcMNlU0sbcxht9lY2zXo69FFEKwqTzE8h11xHSTDLvKD84q4MwCL0IINleG+L8tNdRHU/Tv4GVczxze2dXAzCX7cdpUrjmnAxf3zsV+lCT0VDNr1iw2btyIruvcdNNN9O/fn2nTpmEYBnl5ecyePZvS0lKeeOKJ9M/s3LmTuXPnUltby+9+9zsKCwsBGDFiBDfffPMJPX+7vlM///nPqaysBKCkpISePXsycOBAtmzZQigUIhKJsGnTJgYPHsx5553HkiVLAFi5ciVDhw7FbrfTvXt3NmzYAMCyZcsYOXIkw4YNY9WqVSSTSaqrq6mpqaFHjx7tOXRJkk4xMp5IktSeZEyRpKOzksIUKUOQ6dYIxQ2aYgaBDBs+p8aumigpw6RrroukbrJ9fxSbptCvk498v4PP62Ks/zyIbggGd8nkrA7ewyaJQgh2N0R5c2sNZcEY/Qp9TOh99LWIVeE4z67bx6KtNSQMk+FdAtw1ujtnFnipaIrzzLv7mFdSgUNT+fnwThT7XTy24nOW76jnwp45zLnsTC49K/+0ShLXr1/Pjh07mD9/PvPmzePRRx/l97//PVOmTOFvf/sbHTt2ZOHChfTr14+XX36Zl19+mblz59K9e3cGDRpENBrl2muvTX/vRJNE+Aozip988glPPPEEFRUV2Gw2li5dyjXXXMNtt91GRkYGbrebxx57DJfLxV133cWNN96Ioijceuut+Hw+LrnkEtauXcs111yDw+Hg8ccfB+D+++/nwQcfxDRNBg4cyIgRIwC46qqrmDp1KoqiMHPmTFT19PmHliTp6GQ8kSSpPcmYIknHTwhBJGESjBnYVPC4VGrCCUwTCnx2IgmD/eEUbodKYaZVahpJGBRkOuiS4yaum5TsCRKM6hT6nZzVwYvDdvjPQHNC54OyIFXhBLkeB9/rFCDTbT/i2KJJnTe31rK1phlTQOeAiysGFJLpshNO6CzeWst7u4O47RpXDsjHZdN4vqSM6nCS/h18XHtuEcUB19f11n2thgwZkm6elZmZSSwWo6SkJL0tz5gxY3jxxReZMmVK+mf+/Oc/c8MNN6Cq6mEbeJ0oRXyxuP40VF5ezpgxY1ixYgXFxcUneziSJJ3mZEyRJKm9yHgincqEEDRGDaJJE6cNFAUaIjp2TSHXY6eqKUE0aZLjteOwKeypjaEoCmfku8ny2KkIxtlaFUEBzurgpUOm87BrdE0hKK2N8PH+EAowsMhPz1zPEdfzGqZgzef1rN0btMpgHRqX9S2gZ56HlGGy5vNGlmyvI6GbjOyWRc+8DF7bUs2O2igdM51MObeIgUX+w577y9hW08zW6nC7nQ/gzAIfffK9x/XY+fPns2HDBt59913WrVsHwL59+5g2bRqvvPIKAPF4nGuvvZYFCxagqir//d//zerVq3E6nQghuPfee+nTp88JjbFd1yhKkiRJkiRJknTq0w1BfUQnZQg8DmtD+mjSxOfUrKSwLoYCFGc7aYykKG9I4XNp9CjwgAIfloWoCSfJ9tjpX+TD7Tj83qH10SQbyoI0RFMU+Z0M7hTA4zh8CiKE4LPqZpZuryWUMNBUhQvOyGZkt2wUBTaWN7Ho01rqoyn65Hs4r2uAt3fU89qWagJuGzcOLWbUGdlHXBd5Olq+fDkLFy7k+eefZ/z48enjX5zrW758ORdccEG6omHYsGEMGDCAYcOGsWHDBu655x4WLVp0Qs8tE0VJkiRJkiRJ+g6JJU0aojoI8DhUGqIpTFOQ67HRHDeoCelkODVyPDb21sVJ6CYds5x0zHJSFU6ydX8zhinoXeCha477sDODSd3a8mJHXQSXTWVE1yw6Bw7/WICqcIJ/flJFVTiJAPoWePn+Wfm47Ro76iK89kkN+xrjFPmdTD2nAx9Xhpmzajcum8qPBhUyoU8eziOUvH5VffK9xz37157WrFnDs88+y7x58/D5fLjdbuLxOC6XK91Qq9XKlSu55ppr0l+3lq0CDB48mIaGBgzDQNMOn9AfjkwUJUmSJEmSJOk7QAhBU8ygOWFiU0HToLY5iV1TyPHZ2R9MkNStvRKThklpVRSnTaVvRy92m8JH5WFqwkky3Tb6d/ThdR6aSggh2NMY48OKJpK6Sa88D/07+HEcoZFMJKHzxtYattdGMAV08Du5vF8B+V4nVaEE/7Ohkk+qmgm4bfxoQAEVTXGeXbsPU8D43rlc1q/gqNtvfJEpBOt2N3Je9+xjP/gkCofDzJo1ixdffJFAIABYnUuXLl3KZZddlm6o1eqTTz5pU1o6d+5cevTowfjx4yktLSU7O/uEkkSQiaIkSZIkSZIkfevppqChZesLt10hljIIxU28DhVNVdhXF8emKXTMclDZaK1NzPc76JzjoiacZOveY88iNsVTbCgLUtOcJCfDzuAzcsjOcBx2PCnDZPWuet4va0qvQ/z+Wfn0yfcSiuu88uF+1u0N4tBULumTS8ow+evGSqJJgxHdsrhioJVMHvfrN0xW72rgfzdVsKc+xtJbh33p9/KbsHjxYhobG7nzzjvTxx5//HFmzJjB/PnzKSoqYtKkSenvhUIhvN4Ds56XXXYZ9913Hy+//DK6rvPII4+c8BhkoihJkiRJ0inNbOnK6HOd2N1wSZIs8ZRJQ0RHCMhwKDRGdUxTkO2xE4qmCMcN/C4Nu01lV3UMm6bQu4MHt0Nlc0WY2nCSgNtGvyPMIuqmySdVYbbXNGNTFYZ0CnBGTsYRG9tsLGti1ef1RJImNlVhTI8czuuWRcoQ/GtbLctL69FNwXldA/icNpZsrW3ZH9HH1Wd3oEu2+7hfe0I3eWt7LQs/rKSyKQ6mIJowvtL7+U2YPHkykydPPuT4Cy+8cNjHtza5aVVcXMzLL7/8lcYgE0VJkiRJkk5JphA0x02aEwamQCaKknSChBCE4gbhuFVqigJ1zSkcmoLfbaMqmMAUgny/nYbmFOFQkiyPnW55LmqbU2zc14QpBH0KPHQ5zCyiEILypjgfVjQRSRp0y85gUJEfl/3Qz6oQgm01zSwrraMxprd0P/UxsU8emqKw5vNGlm6vI5wwGNjBS5HfxfLSOmqak5yRm8HPhneiXwffcb/2aNJg8Wc1vLp5P7XhBJiCWNKg0O/kpvO7frU39jtCJoqSJLU7IQSxlEk4blLgP/L+SJIkSYdjmILmhLWOSgiwa6Bx2u/mJUnfKN0UNER0krrAYVOIJq2/+5waSd2kMpjAZVfwOm2UN8RBQPd8Nx6nxscVYeqaU2Rl2OhX5MNzmFnEpliKjeVNVDcnyHTZGNMz94iloOXBKG9sraW6pVHNGTlufnBWPj6nnQ/Kmli8tZbGmE6PHDfDO7tZs7uRVTsb6JLl4q4LujGoo++ITXAON67XP6nm9S1VNEZSYJrEUyZdst1cN7QTY/vkYTvCekmpLZkoSpLUbkwhiCZMwgkDw4SvqfmYJEnfUropaI4bRBImAnBo1rFQzEAABe23LZokfatFkyaNUR1hCuw2haZoClWBgNtGXThJyhBke2yE4wZlDQn8bhtd81xUh5J8VB5CURTOLPTSOdt1SIKW1E22VIXYURvBrimcW5xJj1wP6mESufpIkje21rC3MWY1qvE5+GHfAgp8TjZXhnnjszJqmpN0CjgZXJzJ+r2NfLCviY6ZTm4f2YXBnTMPe97DqQol+OeWKv61tYbmWAphCBK6yZmFPq4bWsz5PXKO+1ySRSaKkiR9Za13/yMJE1OAXQWH3dqTSZIk6ViSuklzwiSaNAFrBjFlmARjJgrgdqjoupxRlKRjMYUgGLX2Q9RUMBSry6nbroBQ2B9M4NAgK8PG/mACgG55bpx2hQ/LwoTjOvk+B2d28OL+QvmoKQS76iJ8vD9MyjA5I9fDgA4+nLZDy0zD8RRLt9eytSaCISDgsnHpWfn0yMlga02ElzfupywYp8BrZ2zPbDaWNfG/H+0n3+vg5vM6M7xLAPU490IsrWnm1c1VrNlVTzJloOsmKUNwbucA1w0t5tzOgeOejZTakomiJElfWspoufvfcnHnsIEChOMGuimwazIwS5J0eEIIErogHDdI6AKEwKYpJHQrYVQQZNhV4ilBfTiFggCOv4GFJH3XJHWrYY1uWrPx4YSOaYLfpRGMpEjogky3RiRhUBlMkOm20TnXzb6GGHsbYjhtKmd38lPgP7R8tCacYGN5kGBcJ9/r4JziAFnuQ5eWRJMGb++oY/P+MClT4LarXNwrl0FFfnbVx3h6zV521cfIdtsY3T2LrVXNLPioihyPnZ8OK+b87tnYjiNBNIVgw74gr26u4qOKJkxdkEgZGKZgZI8crhvaibNOYD2jdHgyUZQk6YQldJNw3CCesu7wu2wKumnSFDMQwvrabVNpPg26ikmS9M2y1jBbCWLKECgIbKpCLGUSTQlsqhVDogmT+oSOTQWPUz0tuhRK0skghKA5Yf0OVhEoQDBmYNfAYVOpbkqiqZDp1qgJJVEUay2iQPDBniBx3aRzloueBR7sX1i715zQ+agyRFkwRoZd47yu2XQKHFqOGk8ZrPq8ng/LQyQM60bxmB7ZjOiazZ6GGH94bx+ltVF8To1hnTPZXhPhtS3VZGXYuX5IRy7okX3Icx9OUjdZubOeVz+qZE99DExBJKFjVxUu6VvAVecW0S3X055v73eaTBQlSTourRd3zQmDpC4AgdOmWHcwo9aMYoZDRZgQiunopsAhZxQlSWohhCCStG4yGSaoikBVIJY0MATYVQWnphCO6xgmOGwKLrtKKKYTjhu4HXLRsyR9kW4KGiM6CV2gKVYjOd0UeBwq4ZhOSDfwuTSiCYOqpiSBDBsdspzsqo1SE07idWoM7RQgK6Pt7GBSN/m0OkxpbTMKCv0LffQp8B0y25fUTdbsbuCDsiBx3brpM7JbFiO7Z7OvMc5/rbUSRK9D5ZwiHzvrovxray25Hjs/+V4xo87IOq4EMRzXefOzGl7fUkVtOIFiCqJJA7/Lxg3DOvFvZxeR7Tn8fo2ns1mzZrFx40Z0Xeemm26if//+TJs2DcMwyMvLY/bs2ZSWlvLEE0+kf2bnzp3MnTuX/v37M336dCorK9E0jccee4xOnTqd0PPLRFGSpKMyTevirjlx4OLOrrXc/Y9aswEeh0YiZVIfTgHgdWqoKgQj+kkevSRJJ9sX1zArikAgWjqaCpyaCkIQiukIIXDbNRRMmqJW/Mj22inMdILseipJaUIIokmTYNRACAGKIJwwsavg1BTqwilrNt6hUhtKoqkK3fLchBM6JbuDKAr0yvfQNdfdpsGLYQp21EX4tCpE0hB0y85gQAc/GY626xBThsm6vY2s3xskmjLRFBjWOZMLe+RSFozzp3Vl6QRxUJGXHbVRVuyoJ9/r4GfDijnvOEtMyxpjLPqkmmXba2mOpcAUxFMmxQEXN4/qysS+BYfdiuPbYP369ezYsYP58+fT2NjI5ZdfzvDhw5kyZQoTJ05k1qxZLFy4kClTpqT3SwyFQtx8880MGjSIf/7zn/j9fubMmcPq1auZM2cOTz311AmN4SsliqWlpdxyyy3ccMMNTJ06lf379x+S5TocDl5//XVeeuklVFVl8uTJXHnllaRSqcNmudu2bWPmzJkA9O7dm4ceegiAefPmsWTJEhRF4Re/+AWjR4/+KkOXJOkYUoZ1cRdt6T6oqaApgkjS2s/MroHbphJJ6NSFrW5qfrfVcruupf31iex5JuOJJH27HNygRgiBqoJumCRbyk0dmko8adKU1FEVgdOmEUsaNEZT2DWFjllOcn0O6iMpPt0fpimmM6Fv3nE/v4wp0reVYQoaozrxlEBVrM6eummt6Q3FdFKGwONUCccMwvEUeT4HGS6V7TXNRJMmhX4nfQo9bRIsIQRlwRibK0M0Jw0KfU4GFWUeMtOYMkze3xdk7Z5GIikTVYFzO/oZ2yuX8iYrQdxRZyWIAzp42VETYeWOBjr4nfx8RCeGd81CO0aCaArBxrImXt9SxYZ9TaR0A6OlQU3/jn6uHtyR88/IOeZ5TndDhgxhwIABAGRmZhKLxSgpKUnHnTFjxvDiiy8yZcqU9M/8+c9/5oYbbkBVVdatW8ekSZMAOP/885kxY8YJj+FLJ4rRaJSHH36Y4cOHp4/9/ve/PyTLnTRpEnPnzmXhwoXY7XYmTZrE2LFjWbly5WGz3EceeYT777+fAQMGcMcdd7B69Wq6d+/O4sWLeeWVV2hububqq6/m/PPPR9O+nXcQJOlkaW0u0Rw3iOsCIazZw5Rh7YkI4LKpmEIQjumYwsBpV/A6NcJxvWUdhEJBpoP8TCemOL4ZABlPJOnbQQhBPCUIt5aoC4GiQMIwMFKgKWBTIJIwiQoTm6Zg1xSrKZYw8bo0irPd2GwKlcEE22siLWV0Gn0Kjn/dkYwp0rdVNGkQjFpNWxTFmpm3qeBQFeqbU9g0cLTMKLrsKt3yXFQ0JdhRl8Tj0BjcJZNcb9sSzdrmBB9WNFEfTZHpsnHBGTl08LvaPCZpmJTsDbJ+XyORpNWNeEChj4t751ARSvLc+nJ21EXxOVTOyvewozbC6p0NFGe6uPX8zgztfOwuptGkwfLSWl7fUkVZQxxMk2jCQFFgVI8crh5STL+ik7NHzkeVIT6sCLXrOc/u6GfQUV6PpmlkZGQAsGDBAkaNGsW7776Lw2H9++Xl5VFbW5t+fDwe59133+WOO+4AoK6ujuzs7PS5VFUlmUymf/54fOlE0eFw8Nxzz/Hcc8+ljx0uy+3WrRv9+/fH57M6Dw0ePJhNmzYdNstNJpNUVFSks+cxY8awbt06amtrGTlyJA6Hg+zsbDp27MjOnTvp3bv3lx2+JEkHMVtKWJrjBroJCgJNgbhhEmu5Y+myqSRSprV5rRB4nBq6KWiK6ghhlZt2z3fjd9uoCSfZXB467hkAGU8k6fRmCkEkcaBEHURL0mhittxwUsRB5aY2laRhEo5ZF4G5Pgc5XhtNMYPSmgjhhIGqQKHfSXGWi6wM+wm1t5cxRfq2MUxr24tYygQEScNEN0xcNpXmuEHKMMlwqDTFrE6nHQIOkoZgc0XYKjMt8NA1290mWWuKp/i4MkR5Uxy3XWVo5wBdszPalKImdZN1exso2ddENGUliH0LvIzrlcvexjjPritnb2Mcr13ljGw3pbUR9jU20i3bze2jujC407H3QaxsilvlpdtqCMX09PpDn9PGNUOK+bdBHSjMdB31HN9my5cvZ+HChTz//POMHz8+fVx84Wb88uXLueCCC1BV9bDfF0Kc8DYhXzpRtNls2GxtfzwWix2S5R6czQLk5uYecrw1y62rq8PvP5BZt54jEAgc9hwyCEvSV5MyBJGEtb2FEKAqoGASTVlf2zUFu6pYjzFNbBq47QrRpKCu2So3zfM5yPfbiaVMKoIJPioPYbYkjr2PcwZAxhNJOj3pxsHrD61ui7ow07OJNlVB1wXhlhkIm2rNJsZTJi67SuccJ5qmsL8pya66CKYAv8vGWR28dMh0HleTi8ORMUX6NoklTRqjOobZcgNGt9YEqkAwqmPXFFQFGiI6XqeKL8PG7voY8ZRVZtq70NNmT8TmhM4nVWH2NETRVIX+HXz0yfdiUw983hK6wXu7G/mgvIlYS4LYv4OPi87IYUddlLnv7aMqnCTTqdE54GR7dYSyYJz+HXz8oG8+ZxZ4jpqUmEKwuSLEP7dU8f6eIEndwNStBLh7bgY/Oqcj487MO2XWHw4qOvrs39dlzZo1PPvss8ybNw+fz4fb7SYej+NyuaiuriY/Pz/92JUrV3LNNdekvy4oKKC2tpY+ffqQSqWsKjH7oVuaHE27NrM5+H+I1iz2SNns4Y4f7tjRziFJ0olr7V4aSVh7lwlhdR7UzQMXd3ZNIWUKQi2L5F12FQVBc8xAAF6XRveAG6dDpSqU4IO9IRK6iV1TKM5y0zHgxO+yfaXPqYwnknRqai0vbW6JIVaCaK2TMkyBqlizh7GklTxqqtISd0wUBXK8djIz7DTFUuyojRJLtcYOF8UBqyrhSM8rY4r0XdK6FjHWsldxQjcwTYFdszqagvX3UEzHpikUZNqpDicpr0rgc9no39FHzkGdQGMpg0+rwuyqj6AAvfO9nFXgxWk7kIzFUwZrPm9gY0UTcd26PhhU5GN092y2VDXz1Jq9NERTZGfYKfI52F4TYW+jYFiXAN8/K58u2Uff6zQc11leWsubn1ZT1hBHmCaxlvLS83vkcOXZRZzdKVN+hoBwOMysWbN48cUXCQQCAIwYMYKlS5dy2WWXsWzZMkaOHJl+/CeffEKfPn3SX5933nksWbKEkSNHsnLlSoYOHXrCY2jXRPFwWW5BQQGrVq1KP6ampoZBgwYdNsvNz88nGAymH3vwOXbv3t3meF7e8S9olyTJ+oXTWhpmCgArKUzoVifC1rVD0ZRJLGk1r3G07GUWjFq/hAoDTgIeG00xnV31UZpiOgpW2VjHgIt8r+OYaxCOl4wnknRqMVo6IEcSBrphJU4Ca19VIQSaomAYgrhhghCoqkIyZcUXl12lU44TAVSFkuysiwKQ47HTs8BDgc952MYUQgiCMZ29wSj7GmP8sG/hlx6/jCnS6aJ1K5mmmLUW0WyZpddU63MYTaRw2FQiCYNo0iTHayOSMtheE8VhU+hX5KXjQXsdJnSTrdVhSmsjmEJwRq6HvgW+Np1MQ3GdNZ/X83FVuGWrDRhc7Gd4lyw2loeYvWoP4YRBvsdOXoaNHbURbJrChT2ymXhWHvle51Ffz/aaCIs/q2bVznqicQNVWOWlXqfGNUOKuXxQBzp8h8tLD2fx4sU0NjZy5513po89/vjjzJgxg/nz51NUVJQukQer46nX601/fckll7B27VquueYaHA4Hjz/++AmPoV0TxcNluQMHDmTGjBmEQiE0TWPTpk3cf//9NDc3H5Ll2u12unfvzoYNGxg8eDDLli3juuuuo2vXrrzwwgvcdtttNDY2UlNTQ48ePdpz6JL0rdTanCaSMIilRMud7gOdBxECm6ZgGoLm1IG1QyYmzS3NawIZNnJ9dpKmoKopwbaa5vSaxN4FHooCLpy29t/fTMYTSTr5hBAkDesmU2v3Umt9lEA3rHI0gER69hCEgHjL7GG2x4bbaSMYTbK1ympM47KpnJGbQccs1yEt91uF4in2NsbY1xgjlLBuSBX4jnwhejxkTJFOBynDmkVMpEwEIv2nCjTHTFSsjWJCMR2vy9qKandDDIDuuRl0z3VjaynZThkm22ub2VbdTMoUdM1y06+DH5/zwOV/XXOClbvqKa2NkjKtcvHvdc5kUJGfkr1NPP72bmIpgzyPA8Nmsr0mgsehcVn/Ai7unYvfdeRUIpYyWLWjnsWf1bCjphnDMEm17PPYK9/DpEEdGNcnH/cR4sB33eTJk5k8efIhx1944YXDPn7dunVtvm7t2PxVfOlE8ZNPPuGJJ56goqICm83G0qVL+d3vfsf06dPbZLl2u5277rqLG2+8EUVRuPXWW/H5fEfMcu+//34efPBBTNNk4MCBjBgxAoCrrrqKqVOnoigKM2fOTC/UlCTpUIZpNaeJJKzmNK0Xd62lYZqioIK1F2LCKg07eO2Q065SnOXEZlOpbU7yUXkY3bSSyM5ZboqOUloqhKAhmqIsGKMsGOMHxzEDIOOJJJ1azJbS0eaESbJlxvDg2UMVMAxBUrduKClK6/fA7VApDDhI6CaVoQSxVAxNtRK9jgEX2Z7DN6YJJ3TKGmPsDcYIxqw9WfO9DnrnB+gUcLUpjzsWGVOk040QgnDcJBQ3MISJ3nIzRlMU4smW390tPQPsGgQ8NvaHEiR0ax1irwJP+sZLyjDZURdhW00zCd2kONNF/w5+Am57+rkqmuKs2FlPWTCGblqdUkd2y6Jzlpt3dwd54u3dmEKQ7bYTTejsqI1Q6HNw/ZCOjOyeddS1g3vqoyz+rIYVpbVtmtM4bSoXn5XP5QM70KfQK8tLTwOK+GJx/WmovLycMWPGsGLFCoqLi0/2cCTppDjc7CEIdFOQaikT0xSFZMteRAiBpikkUtbXqgJZHhsep0YobqR/AWmqQoHfQVGmi5wjXOCZQlDbnKS8KUZ5ME40ZaRnAC7skfuNvxdflYwp0ndVUjeJJK3Zw9bGGbpptpSaggIkUiZGS7KYMqwYo6kKWR4bKFDXnCQY0wGrtLQo4KLA58SmHRo7QvEU+4IxyoLxdHKY63HQOeCmU5abjJaL0UjSYFt1mHM7Bb6hd6L9yHgiHUsiZdIYNUgaJqbZUvGDQNetmzGaajW0MU2BL8NGQzRFJGmQ6bbRp8BLlsdKAFOGSWlthG01YZKGoNDnZEAHf3qdohCC0tpmVu1qoLo5iSnA49AY2S2Ay2Zj9a5GdtZHsSngc2qUB+MkDUHfQi8T+uQxsKPviB1MYymDNbsaWLatlk8qQ+i6ac0gGoIu2W4mDerA+LPy8btOrJmKdHK1a+mpJEnfPN0URBMmkWTLuiGsC7r0nX8FhGmVgomDGkvEUyakINNtw+e2EdcNqkIJmuuN9LrDokwn+UdYO2SYgqpwnLJgnIqmOEnD6sJW6HfRP9NHcaYbx9dQkipJUvtq3R4n0jJ7aLQ0bkkZAtO0ytVT+oFk0apOsO4x+90aTptKKKHzeX3UuvB0avTK91AUcB4y6yCEoCmupysOmuJWQpnrcXB2x0w6BVx4HNalSUM0xQf7gnxSFaY2YiWRp2OiKElHYpiCpphBJGFgCEFKt0q4ERBLGi1LRazmUW6nSiSpUxaM43FonN3JT77PgdJyA7i0tplttc2kDEGR30W/wgONbFKGyceVId7b00hjTEcAORl2RnbLIpQweKu0kepwApdNxWNXKQ/GCUYVRnTLYnyfXDpnHb5BjRCCbdXNLNtex+qddYRjOooQRBMGNlXhgl65TBrYgYHFfjl7eJqSiaIknYYO13UQYbWVNqxONai03PlvudCj5WuBVRrWIeAgJQR14SR7g9b6hqwMqy19od952CQvaZhUNsUpb4qxP5RAN60OqUV+F50Cbgp9bdvZh+MpfPLuoSSdcg5eexhLWsmhaVqzh9ZMYkuVQkszGvWg0lKnzZo9jBsm+8MJUoYVBzodoSxdCEFjrLUcPU44YSWH+V4H5xZnUpzpJsOhIYRgfyjOqp0NbKttJpwwAKvRVsCl0Slw9G6KknS6EC37jjbFDGvGvmXWXuHAel9TCJIpgdOmYCiwP5TAaVPpV+SlKOBCVRQSuklpbZjtLQlix0wrQczOsBLE5oTOur2NfFQZItLSObVjppOhnQPsaYjzykfVNCd0PA4NFagKJfC7bFwxoICLeuWQeYTf302xFG/vqGPp1lp210cxdBNdt9YeFgdcXPe9Tny/XwFZnuPf2F06NclEUZJOI0ndKglrLQtr/WXSeudfbbn7mGq5oNNUaybAaNkTMcdnx0TQEEmxvzoBgM+l0TM/gw6Zh28sEUsZVDTFKQvGqGlOWB0MbSpdszMoznSR7z0w42gKwY7aCB9VhtjTGCOSNJh5cc9v9D2SJOnIzHTnUpOUcSCOpAwrIbQuVFs7I9OSOIKqCLxOG3rLGuTqSBJNhXyfkw6ZTnI9bTseCyGoj6Yob5k5bE5alQr5Xie9870UZ7pw2zVShsnOuggfVYbZ0xgj0bLm0a4q5HvsdPC5iKUMPt7fzIZ9Ya4Y0OGbf9MkqR0ldJNg1CChm+iG9TlEWL/fdaO1n4DVGErToDqSxKYq9Crw0CXbjaYqxFIG22ua2VFnNYgqbkkQs1oSxKpQnNWfN7CrPkqyJQHtlZvBGTkePt7fzLz1FeimiceuEUnoBKMpuue4uXJgIcO7Bg67f6lhCj4sb2LZtlrW7W4kltRRTOsawWlTGXdmHpf2K5Szh98yMlGUpFNca2OaaNIqCzMPLgsTbe/8i5Y7/3rLuiFVAZ/bhqJAUzyVbknvdVrJYYHfidfZNgy0loZVNMWpDMWpiyTTP9Mrz0ungIucDEf6F0E4kWJTeYhtNc3pNQ8ATk2hc0C2upakk83aoNsqUY8mjUNuMAkhMMyDv7ZK2hUgw6liAsFYioamOIpizQQWHqYs3TAF1eEE5U0xKpvixHQzvVb5rAIfHTNduOwaobjOxvImPq0Kt40ZNoVOmS78ThtVoQQfVzazNtEEgKYqR+yQKkmng4PLTK3O49bvbMOwqoFoWTICoKiChpiOokC3HDfd8zKsvRPjOltrwuxuiCIEdAq46VvoI+C2YwrBtuow7+y2ykgNYTWo+V6nTJw2lfV7m1i7pwlVsSqOQjGdWNJgeNcAY3rmckZuxmHHva8xxsrSOlaU1lEViiMMQTxlIAT07eDj0v4FjOmdh8cpU4pvI/mvKkmnoNbNqaNJs2XTatJNJVobTAihkGgJ1spByaEC+NwaKBBOGOxttMpKvU6NHnkZFGYemhwapqC6OUFlS3IYSVolX9kZdvoX+igOuMlsKSczhWBnXYQPK0PsbTzwWFWBTJeNbtluumZlUFoT4cPK8Df6vkmSdEDKMNPbWuiGwBBW/DBMKyE0TdKzitbjrT9ddgW7XSUU12kMpVCAbI+dM/I8FPgdbWYbkrpJRShOxUHl6DZVoYPfScdMN0V+F3ZNoaIpzrLSOnbURdIlpaoCXodKToYDUCitibJ1f0M6cbRrCvk+B4M6+jmn2E+2W5axS6cfUwia4yahmE7KtDqZtn4Gky37GLfeBFZUaz9DRYHOOW6652TgtKvUR5NsrQ5TFoyjKtA9x0OffC8+p41YyuDd3Q18UBYkFDcQQMBlY2CRn7pIipU7G62Oo5p1zRBLmeR7HUw5pwOjzsg+5HoArBtD7+ys5+0d9WyrCpPSTTCtzukBt53LBhRySb8Cuud6vvk39Dtm1qxZbNy4EV3Xuemmm+jfvz/Tpk3DMAzy8vKYPXs2DoeDbdu2cf/99wMwduxYbrnllnS358JCq/v8iBEjuPnmm0/o+WWiKEmniNY1Q9GESTTV+ovE+lNvuahTFOsXimFai91bZwUAPC4Vl6IQSRrsC8atYw6NM/IyKPQ78X1hr6NYyqAyZDWiqQon0q23C31O+hb4KGopDQNoiiVZtauB0tpmapqTGAfNGnYJuOiR6yaWEmwqD/HGp3VEW5JHWXwiSd+sL1Yg6C2bdeuGSMeNdAzhQHJo08Bht+JHsNn6/GZl2OiS46XA72yzV2okqVPe0sSqpjmBwCpH75LlpjjTTYHPiW4KPqsO887nDZQH46Ranq+1pDTDrtEQ1dleHSGSbAZAVRRcdo1uOW6Gdg7QMzeD8sYYH+xt5Il/VbG7Psq7d4/8Bt9NSfryRMsWM8GoTrIlQdRbOpAnUlYH09bKIFRrexhFgc7ZbrrlunHaVKrCCbbuaaa6OYFdUzirwEuvPC8um8r+UJwl22rYWRcl0fI57pzppDjLzY7aGP/4uBrDFDg1hVAsBQqc3dHP2F459OtwaPfSpG5SsjfI26V1fLAvSDypo7Q01dFUhWHdsrikXwEjumcftjRVan/r169nx44dzJ8/n8bGRi6//HKGDx/OlClTmDhxIrNmzWLhwoVMmTKFBx54gIcffpgzzzyTu+++m1gsRjQa5dprr+WGG2740mOQiaIknWQpw9pfKH3X/+A1QyYoLZtbH5hJJL3dhduhYrOpNCd1KpqsroBe54Hk0OvU0iWirQ0lKpviVITiNEStx2fYNbpnZ1CU6aKgZb1hQjf4ZH+Yz2qaqWxKEDtor7SAy0bXLDceu8a2miibysOsKG1Ivx63XaVPgZchnf2M6BL4Zt9MSfoOsioQrDgSS5qHxBCBSG9yLSB9saqqoGkK0ZRBMGF9xrMybHTK9lDgc6Y3wRZCUB9JUhmKU950YBsLv8tqzV+c6SbbbWN/OMGmiiZ21UdpbNkeQwFcNoVcjwMhYHd9jG1VEVr35bJrCgU+pzVr2NGHbpp8VNbEqxsrrBb7LTOU2R4HOT7nN/zOStKXk9BNmqIGsZTVjTxlmpgt+4623vzVWxrNhZM6KNAp20X3XKvEdG9jlO21EYKxFG67yqAiPz1yPQjg0/1h1u1rpD6SwhBgUxX65HlQFYWPKsNsKG9GwUpG47qJ5rbxg375XNQjh1xv2+YyQgg+rWrm7dI63tlVT1M0BaYgnrRmJs/q4GP8mflc1Cc3vf5R+uYMGTKEAQMGAJCZmUksFqOkpISHHnoIgDFjxvDiiy9y8cUXE41G6du3LwD/+Z//CUAkEvnKY5CJoiSdBLohiKasstLWu/7ioIYSiJaSFJP0OsTWxzhsKna7QnPCoKnZuhjLdNvoVeChwOdos04goZtUha2SsKpwnFjKuhjM9TgY0MFPx0wXmS4bQgh21Ud5bU+QfQe1rAfIsKt0z3YTcNnYH05SWhNh475Qm/Kwzlku+hf5GNk9m9wMO5/tD7NpX5DpH1Yy9+qB39j7KknfFa37plqzh0ZLaak1a2GapOOJYXKg1FRYF6aqphBNmsRbuiBme+x0y80g3+dIb2eR1E32NUapDCWoDMXTa6dyPQ4GFfkpznSjKvBxVZh/flqdLjsFsKkQcGo4NJX6qM6O2hjxlHXBoqkKHqfGGTkZDOkcINdjY2dNhA17Gvnb+r2E460xzY7XbSdlWvu9ulw2+hf5v+m3WZJOSMoQhGJ6ulmUbprWGkT9QNl3yhCYiiDSsv1FcZaVIKLAjroIO+sixHUTv8vG9zoH6JqVQTCWZOn2Wj6rbiba8ns8y22jONNNZVOCd3cH0Q0TTVEIxXXUltnD0T2yGVjkb7OWWAjB5/VR3tnVwOoddVQ2JTAN02p8Zwo6ZDoZf04RF5+VT+fsw69b/C4q2Rdk/d6mdj3nsC6ZDO0cOOL3NU0jI8P6N1iwYAGjRo3i3XffxeGwkva8vDxqa2upqKggJyeHhx56iO3bt3PxxRdzww03EI1GWb16Ne+88w5CCO6991769OlzQmOUiaIkfUNaS8JiSdPqdvaF5FCYB2YAWptNGC0XfA6bgmaDcNygKamn1wx98eLOFIK6SJL9oTj7W2YNBdaC9kKfiw5+J0V+q6FETXOC9fuCfF4XoS6aOpD4qQpFPgfZGXaCMYMdtVE+3R9Jl6ppqkK+10mfAg/nd8+iU8DFZ/vDfFTWxGP/2s5n+8MHrXWS5SmS1F5ay9NjSZNIwiDVcuGpHxQ39Jbk0DQPVCeggKpae6kmWhrMWGsOM8hvKSttbWL1eUPUamLVnEzHjg5+Fx38Lgq9DiqaEny0P8TirbWEDyoxd9lUfA6NWNKkLBhP32xSALtNpWu2m3OKM+mW7aK6Kc7m8hD/tXIXlU1WmbzbrmG3q7hdNmyaij/DTv8iHwM6+Olf5KNzllt2UpROWbopCMUMmhOGlSAaVoKY0s305zRlmBhY/Qc0FbrmuOma4yaWMti8P8TeRmsf0iK/k155XnIy7OyojfDCB2VUhRPoprWut2uWC7um8cn+ZrZVR1GwlpKkDEGBz8H4PoWM7J5NVkbbNb37GmO8s7Oe1Tvr2dcQQzdMREsjHa9TY3z/Ai4+M58BHWXX0lPN8uXLWbhwIc8//zzjx49PHxfWxrYIIdizZw9PP/00LpeLyZMnM2LECIYNG8aAAQMYNmwYGzZs4J577mHRokUn9NwyUZSkr5FpHmhKE0+1rhdqe9dfN0z01uTQFOnGNTabAqq1V6KRFGgK5HodFPid5PkONJSIpgw+r4+kZw1bW2FnZ9jpW+ijg99FdoadUDzFR5VhVu5soLo5QbIlmVMVyHLbyXbbiSYN9jTEWVfblE72VMU6V698D8O7ZtEty20lhuVNzFm2g+3Vzekk02FTUVSVDIdCVoZDzgBIUjtIGVYMiSSsi0HdFBiGte2NedDaJyuGtMQSAAViupEuccv1WPEj3+fAYVPRDdNqYhWyGllFU1biF3DbObPAS5HfhSkEW/Y3s2RbLbWR5EE3lMBrVzFMqG5OsjOcTJeT2lSFAp+Dswp9nJmXQSiWYktliAUflFHW0lzLrik47Roupw2bTSHTZWNAx0wrOSzy0znLfcgaKkk61RimIBw3CMUPJIi6IdB10/qsGiYpYc32x3UTu6bQIy+DTlkuaiNJ3tvTQE1zEk1VOCPHQ8/cDOIpk5KyJrbXWLOHAvA4VDr7XNQ1p/hgX5iUYd3wiSQN7JrC0M4BRvfIpk++p02Stz8UZ03LzOHOuih6yrRuOOkmTpvKiDOyGdcnj+Hdsw+7d7J0wNDOgaPO/n1d1qxZw7PPPsu8efPw+Xy43W7i8Tgul4vq6mry8/PJycmhZ8+eZGVlAXDuueeyc+dOLrnkkvR5Bg8eTENDA4ZhoGnH30FaJoqS1M6MluQwljSJpQ6sF2rtdCYE6PqBizyzpTW9idVQImVaJSkkwWlT6dByYZfjdaCpCropqGtOsD+cYH/owJ17t01t6TLopMDnIqYbfFwR4oOyJmqak8RbSscAvA6NPI9GPCWoCif5sC6UThwVrLKvHrluhnUJ0D3bzdaqZj4qb+Kpt3bweV0U0fI4TVOw2zU0TaU44KJfkZ8zC7z07eCjU8Al70pK0peUOmjmMGmYB5JDk3SJqZFe03ygCkFg3TwSHOgamu93kuuxo6kKTXGdXfURqsKJ9L6ottYmVn4fPoeN0roIG8pDVIdrD4oLAoemgFBpjKWoDifbVBn43TZ65XnoV+ghkTTZWhXm7U+reKHBSgxtqoLdpuJyataModvGgKIDiWGXbJkYSqeP1k6mTTHd+nweJkFMmgJdmCQNgdOm0rvAQ57Xwd5gjCXba4mmDDLsGoOKrGUg22si/HVTJfVRa+2hgiDX40RBobQuwo4a67MUa1k/2CM3g/O6ZTGiWxaeg7aOqQkneG93A6t31rOtqplkS8fSpG5iUxWGd8/mot65nHdGjtxy5hQXDoeZNWsWL774IoFAALA6ly5dupTLLruMZcuWMXLkSDp16kQkEiEYDOL3+9m6dSuTJ09m7ty59OjRg/Hjx1NaWkp2dvYJJYkgE0VJaheGaV3URVPWzGFrx1I93YqelrJS66KutTGNiUBRFGKGkZ7By3Tb6JGXQZ7Pgd9lQwAN0RTbapqpClv7Gpot+yXmeZx0K8qg0O9Cwbrzv2JnPdXhtomhx66Sm2EnnjKpaU6ytz6Wfj6l5Tn75WRwbrGfLlkudtRE+Ki8iSeX76SipYOqooCqKjgcGk67Sq8CH/07+Dir0EefAu8hZS6SJB0/a/ubljWHCZNEy42lA630aak+EAeVlYJQrJ9r/bxnOFS65LjJ9zkIZNhb1ikn+KDMSg5bH+d32eiZ6yEnw05VOMn22ghr9wTTjasQAk1R0IBQ3KAukmxTZZDh0Oiak8GZeRkoQrCrNsKGz+tZ8H4ZgPWzNgWXQ0OzqRT6nfTr4KdvBx9nFXrljKF0WkoniHGdpG6tQzR00ZIsWuWlSaOl3FQIPE6NHvku7DaVXfVRSsoaEUCB18nZHf2YQlCyr4nXPqlOdy71OFR8DjtlwThb9jdjGGa6oV2e18HFvXM5v1sWhf4DzZ3KgzHW7m7k3V31lNZE2mxnoakK3+sS4KI+eYzskXPY7TCkU9PixYtpbGzkzjvvTB97/PHHmTFjBvPnz6eoqIhJkyYBcN9993HbbbeRSCQYOXIkffr0wev1ct999/Hyyy+j6zqPPPLICY9BEa0Frqex8vJyxowZw4oVKyguLj7Zw5G+I3Sjpaw0YRDXBYZptvzHgUTRID2j2HphhwImwrrrL6y78bkeO3k+B3leJw6bdde/OpyguuWuf2tr+YDLRoHPmjF02RS21kTYWRelOnygMymA26bg1DSiKYO65lRLd7SDSk0z7HTPyeDsIj+ZTpXtNRE+LGtiS0Uo3dFQUayxaZpKZoadAUV++hX5OavQS49cz7e6TEXGFOmbIFq2t2mNIwm9Zc1hy7Y4hmGSMtpWHqRMa81hquViFCDgtpHvsyoPnHaVukiSqnCCqlCcYEvFgdOmUuB1kuexE4ob7GyIUh6M09yyzlAIq2QdAc0Jg/po6sDNJMXqjtwp4KJ7jhsN2Fsf5cOypnQpqaqApqmoLTOH3XMz6NvBT99CL2cV+sj/DncslfHk9PfFGcSUbiWGSd1MJ3IJ00ocBZDjsdMx4CKUTLGrLkoooePQFLplZ5DjcVBaG+GTqjDhhIEpQFOsku9IwmBvYzx9YzlpCNx2lWFdApzfPYteeVZpaWtDmrW7G3nv8wZ210Wt5LClrFRR4NzOAcb2yWNUjxz8cg9ShBDsqw1TsrWK97dX8f62/bzzn5NP9rBOee16W+GTTz7hlltuoUuXLgD06tWLn/70p4fdGPL111/npZdeQlVVJk+ezJVXXkkqlWL69OlUVlaiaRqPPfYYnTp1Ytu2bcycOROA3r17p9vCStI36eCLutaGNAfu+B+UHJoHkkOz5cJOUbF+ubRceGU4VDpnuclraRoT0w2qwgk2VgSpPuiuv9eh0TnLTb7X2pB6Z12UjRUh6iL16S6EAE7NKj2NJU3qoymCsRStt4A0VSHHY6dnS1mYMASf7A+xcW8j/7ehPN2pUFHApqm4nDaKAi7O7pRJv0Lr7n9R5jdfRirjifRt1KYhTdIgqR+YNTyQHLZUHrSsQ0yZ1sVnsuUi9OCbS7keB1HdoDqc4IPyILXNCYyDKg7OyncRTuqUBxOU7AsSTlhla0IIRMvNq0jSIBjV04mnqljNZc7IcVGc6QRT8HldhPd31rHk41T6Maqq4nRoOO0aZ3bwWjOGhT7OLLQ2Aj/VyJginSjTFDQnTIIxnWTKaiCl6wYpA1K6QdIU6RlEVYGiTCe+DBtV4QTv7mnAEIKcDDsDO/hojOus3xekIaqn+xV47BpCQFkwzt6GeLorqqrAwCI/I7tnMajYj0NTMYVgW3Uz7+1u5N3PG6gMxtB1s6URnsCuKQzpksXonjmcd0YOge94lU8knuLDnTWUbKuiZNt+PthWRVVjFAC308Y5PfJP8ghPD+0ayaPRKOPHj+c//uM/0sfuu+++QzaGnDRpEnPnzmXhwoXY7XYmTZrE2LFjWblyJX6/nzlz5rB69WrmzJnDU089xSOPPML999/PgAEDuOOOO1i9ejWjR49uz6FL0mEJIYjrgnhLC/qU0faOv7VOyFoz1FoOZrQsXDchncy1Jmu5Xgc5HjsCqGlOsLM+Qu3eRPquvsumUuCz1hMldcHnDTE+KGuiMaanEzoAmwKY1lqkYExPb3APVpfCokwXvXLddM1y0xxPsXFfE8u27Oev65Lpx7XOFuZ6bZxZ6GNgsZ/e+V565nlOidIUGU+kbwtTCBIpQSxpEEke1Da/pZzUaGlQ0xpDUgfFkdbPvdepURRwk+u1o6gKtc1JPm+Msr6s8UDZustG5yw3zQnrxtOmilCbGUNrHZUVN8JxI33u1lLSbn4XBR47pmmyvaqZ1Vur01vqtM4Yupw2sjJs9O/op0+BVXreK+/0qDCQMUU6XoZpbXMRihsk9ZYmUro1c5gyrBvFKdO6kePQFDpnu0gJwZ5glOZaA5uq0CngQgU+q4nwQXkovf+xXVVx2TQqgnEqU0kM07o5pAB9C70M6xpgcKdMvE4b8ZTBxn1NvL8vSMmeRmrDCesGUsu1iNuuMaJnDqN65DC8exYZjpP/u/tk0A2Trfsa2FhazcYd1Wwsrebj3XXpddRnFGVy4aBODO3Tge/1KaRLYSbljfGTPOrTQ7v+H3W4jR0PtzFkt27d6N+/Pz6fD7A68WzatIl169ala23PP/98ZsyYQTKZpKKiIr3h5JgxY1i3bp0MwtLXxjAF8ZTZsnl1a1LY0pn0C3f8TVOgt1zMCVraX7fkc36XjY4BFzkeG5qqUhdNUh6K8WFlU7q7oENTyPM66ZbtJpww2BeM8/6+JkIJPd1dEGECCildEE4YhOJtk0aPQ6NXnofOAScBl0ZZQ5yPypv4YGedtYid1jJSlQyXjS45GZxd7KdvBysxLPA5TsmmMzKeSKezg+NINGkeaI9vHkjYWmcQW2OIiTUzcPCsYY7XjsuuEozr1DQn+KQmTNI4UHGQ53HQnDCoiSQpqzqwx5phtrTmNwTRpNW2vzVqqIoVn/K9dgJOG7GkwWf7w6woC6YvrFRVQdMUPG473XMzGNDRalTVp8B3ysaMY5ExRTqW1n0Qw3GDpGGg61YpZ1K31h4m2pR8a7idGg2xFB9XhRBArsdOttvBvmCM1Z83EtetLqOqouDSNPaH4kSSZpvksHe+h2FdAwzpnEmmy05NOMGqnfV8sDfIprIgsaRhVSfpJkJYN4RG9slhdM9czu0cOC1u0rQnIQS7KpvY0JIQbtxRzeZdtUQTLXuwehyc06OAe64azJldc8nO8tIQM/i8NsKm2igLXiulNpwAYPNDY07mSzkttPuM4saNG/npT39KLBbjtttuIxaLHbIxZF1dHdnZ2emfy83NPeS4pmmoqkpdXR1+/4EW+63nkKT20lpSGk9ZF1TxlInRur/hwbOGrXf5zZb29F+44++0qRT4neR47DhsKo3xFDXNcT6pSaZnFl02lTyPg0LVQV1L58Bd9UFiLS2woaVs1bCaUzQnDKsDagtNVcjOsFMccJLjspNI6nxcGWZtaQ3LEgcep6oKNk2hKMtN3yIf5xRn0qfQS9fsjPS2Gqc6GU+k08nBcSSSMIi3rF1KtSaDB21roZugCzMdR1rv+/hcGkWZdjJcGvGUSW0kye6KaDp+uG0qXqeNcDxFQyzF3sZ4+kI0ZZgkdUFSb4ljB5Wm21qSzoDLhktT2B+MU7o/xEct6xfB6mCsaSq5Pjv9OvrTieEZuR6cp8iFaCyh4/4K1Q4ypkhHktCt9YeRhEkyZbSsQxSkdIO4IUgahlXSjSDHa0cXgopQnGSTwG1TyfU4qGlO8mFFmFjKTO+D7NAU/j975x0mVXn2/8+Z3rb3Rl16RzqCIgiiJpaoCGJJNWqIJv6sL2ry2hAxRo2JeVXEaAqCPSIgKlgoAiKC1KUubO/TZ075/XHOzO7SF4HdxedzXXOdM2fOOfPMwtzzfJ+7VQdk/BElbgNAr1g6opPebiHJaWFHhZ93vy1nzb5aimLFaIx8Q4AOqU5Gd0llVNdU+uclYTa1v4Wak0HTNA5W+1i/oyLuKfx6ZwV1fl3oOe0WBnTJYMq4nuRkJOLyuPCrEnuqAnxa4uetov3xe7lsZjpnuBnRJYUuGW66ZLhb62O1K06pUOzZsye33XYb48ePZ8+ePfz0pz9Flht/iJo2hmyKpmnx5NxDjx/pmEDwfYmt9oeMHof6Sr++yh/LF4qFgslNHiqNht5ihJMmOa2YTOCPylT5o+ys8cVLyrusZlKdFsKyRnUgSnFdiM3lPgyHgN5jSYlNMPWcJaWJt9BlNdMx1UGmy4ZZ0thdGaCotJ61RdH4ObGQsIwkO90yPAwuSKJnloeuGe42mSd0ogh7ImjrNA8pVYzvcqP3UJZ1ERg1qpXKqu49jH3FHRYTmW4bNqtEVNWoDUbZUuWLC0O7GUAiomjUBaM0hGS9rY6mv2dU1qsaBoxKy/rZ+uQ0y2PDbZUIRxT2VgfYXVoff99YPrLHaaWz4S3sm6NXL053t763MCor7Cqp57t91WzZVx3f7iqtx//+jJO+r7ApgqZomp4rXBdUCERkorJGRFaIRHXPYcRocQFgt0g4rBK1wShFNQEkNDx2C4GozPaqACFDHMqK7j2sCciEooo+n9DrT9Ej083QDkmcU5CExSSx4UA9c1fvZ+3+eqp9YWRZjYefm00SgwuSGNU1lVFdUslLdrbuH+sMoGkaxZU+Nu6qYOPuSjYUVfD1zop4XqHFbKJbfgoj++WTmpKA2WHHq0jsrQ7wZZUCVfVAPUlOC10y3FzQK4MuGW46Z7jpmuEmK9He6ratPXJKZ5Fdu3ala9euAHTu3Jn09HRKS0sPawyZlZXF8uXL49dVVFQwcOBAsrKyqKyspGfPnkSjUTRNIzMzk7q6uvi5sXsIBC2hsYCEQiCiT65kpWmeoRZvZRHfHuIxNEuQ4rbhsZvRJH3VviIQZnu1L+4NTLCZcdnMqCGl2Yo/gKqqhGT9hyQcVQlEG1tiAFhNEqlOC0l2M1aTRK0/wp7qANsOhJtN8MwmE+kJdrqkuxhUkEy/3IR2LwqPhLAngrZGvKBVk1Y48f5pTbyGshFWJmuNecugLy6luq1YLSZkVaUuFGVrlQ/FEBxWs4mIouINKdSHZT3SwPAqBKNqfEGpaSErkwQuiwmXwwyaRo0vwv7qINujjeeYzXpbm/xkJ/1ydW9htww3BSnOVvVMqKrG/oqGJoKwhi37qtleXBP3pJhMEoW5yfTplMY153X/Xu8nbIoA9IVib0imPqjnH4ajMW+87omPqKreegYNh81EIKpQFpBB07BbzURklTJfhFA0aPQ01RcSavzR+NxCNSqa98tJYGiHJPrlJlJSF2R9cT1/2FzGznI/sqIXoon9X09xWRlleA2Hdkw+q/MNFUWlqKSOb3ZV8s2uSjbuquTbPZVUNzS248pOSyArM5lOXXJQzFYaVBMKErtk2FUZJSNBonO6mx8NyIl7CLtkuEl1W4UgPIWc0v+FCxcuJBAIcMMNN1BZWUl1dTVXXnnlYY0hBwwYwMyZM2loaMBsNvP1119z//334/P5WLx4MWPGjOHTTz9l+PDhWK1WunTpwrp16xgyZAhLly7l+uuvP5XDFpyF6D3JiAuyYKyAhNJcGDZf7ddQtMYcQ5Ok9xd02EyogD8ic9AbJFCjxF93GCFZPiN30B9VjNxFlWBUv2dEVo84uUu0W3C7TaCq1AdkSuqDFJVEaLoebTZLJLttdEpzMTA/icEFSRS2kWIzR6KqPsiOg7XsPFDLjgN17Cyp5Y2Zl57UvYQ9EbQFGnMNYwWtGu1IVNFQFI1oTBiqeoNttYkN8djNujDU9N5rRTV+NEBR9dzjsKziDSv4InpxmajSWFU5IqtGaKl+PwmwmCDBJiGp0BCMUuYNNytmFQs7z0910is7gSEdkumR5aFjqrPVws5lRWVPWT3b9tewrbiW7Qf07bb9NfhDjdERHTIT6NMxjYnndKRPxzR6d0yjR0EKjlM0YRY25YdLbLG4PqDgDUXjwjASVQka+Ye6t1/DYtb7k9aFZNSw7iFsCMnUBqP64pCmt8dSVC3eLkMxcovtFhPDOuj5hlkeG9+VeVlRVMXTn+7CF1KMQjRqvBdyn5xEhnZKZlSXVLpnec7K3qLhqMx3e2vYuLuCjYYw3LSnKp5TaDabSEt240lOwJmeimSzY3c6MJlNaFYT6akuOqW76JjmomO6i05p+v4PpeXH7NmzWb9+PbIsc/PNN9OvX78jVmretm0b999/PwATJkzg1ltvPWql5pZwSvso1tfX8//+3/8jEAgQiUT4zW9+Q69evbjnnnsIh8Pk5uby+OOPY7VaWbx4MS+//DKSJDF9+nR+/OMfoygKM2fOZO/evdhsNmbNmkVOTg5FRUU8+OCDqKrKgAEDuO+++5q9r+hRJADdcIdlNV52PjaRa/QcavHS00fKDzJJ4HGYMZslFFXDF5GpC0ZRNP1HxixJyJqGP6LiDckEonrOgqyqhKKaXjrbGEOkiadQQm+HYTNJqIqKLxSlwhvB2yQ/KBYKlmaIwn55CQzMT6JretsTheGozK6SenYcqGXnQUMQHqxl58E6aryNVcSsFhNdc5LY8MLJTZpay56AsCk/ZOLhpFG9CE3Y+F43eg51j2FEOTyUVELDaTNjMkNE0WgIRQnKKqqqEpZ1D2PQCDGPGPcMRnQxqFdS1JqFnpskDYskgar3WazxR5qLQknCYpFI99gozHQzuCCZvjmJdE5ztUpeYTAss+NALduKa9hebGwP1FB0sC7uNQHITXPTsyCVnh1S6dtJF4S9OqSS6Grea9EflimpC1FSF6K0LkhJXYjfT+p20uMTc5QfHoqq5wzXBxWCEZmwrBI2cnhjhWlUTU8r0SQNn5H+IauqUTxOibe2ivdOjipE5cYFoTS3lSEFSXRNcxKIKGwp8/F1cR0ldXo/RM2YfwDkJNkZ3imVYZ2SGdwhuc39vn8fNE2jtMbPd3ur2by3mm/3VPJ1UQW7DjYWybJYzLjcTkx2G3aXA4fLid1pJzfFScc0XRB2igtCPVzU9APJxzwSq1ev5uWXX+bFF1+ktraWK664gpEjRzJ27Nh4peb8/HymTZvG1VdfzR/+8Ad69erF//t//49HH32UxYsX8+233/LQQw+xYsUK3n77bf785z+3aAynVCi2FsII/zBRVCM/J9xouOMTupgwNHIM4qGkqhb32JlNYLeaQDIS2cPRePlqWdXQNAhGVXwRhWBMFCp6yFlEVuP5QU0rkIKG1SRhMsJR/GGZukCEUJMwMEkCh9VMZqKdwgw3g/KTGFSQRH5y64aBNSUUkdlTVk/RwTp2ldazu7SeXSV1FJXUUVzppanVyE510z0vme75KXTLS6GbsV+QkUBDSCa9HTbaFjblh4Oq6QVgglEFf9gQhqpeCj9q9DiMqrrXMBaWrtKYi2a3SWhAUFbxhWUU1QhjUxvzB8OGxyIY0aMLokb+UyxXWc91A5Om92INRRUaDO9FDEkCu9VMVoKdbll6LnLv7IRW8RRWNwTZfqCWHcXNReG+ioa4bTCZJLpkJ9GzIIUeBan0LEilR0EKPQpS4oLQG5IpqQtSWhfSWwUY+yXGfn1QX0xTFZVoVAFVZduTF5/Rz3oqEPbkzKJpehRAQ1DBF9ZzBcMRlaCsEJaN77Kmi0FN0ow0EH3xx2sUkIsYtQMiikYgLOsLPUYfU5ME3dLd9MnxYDNL7K8J8nVxHftrgsiK0U/ZEIZOq5mhHZMZ1imFoZ2Sz5pcQ18wwnf7qvl6ZyVf7Shn894qdpfUEWgSIWC2WrA7HThcDuwuB2mpHnrkJdMp3W08dM9gQaoTh9Xcip+m7aIoCuFwGJfLhaqqjBw5ErfbzeLFi7HZbKxfv5558+bx0EMPceONN/LBBx80u/7uu+/m8ssvZ9SoUSiKwvnnn8/nn3/eojGcPUsZgrMazaggFooqBI3V/qgxkYuqetGImEDUhWHzlX5V0zCbwGqNeQUVQhEVJaBP6hSjX2Iwqq80ykYFwVhekF5RsDEsVR+TimQ0rQ5H9UmiPyQ3Cx01mSQSHBY6p7vpkeXhnA7JDMxPIqUNNMINhKLsLqtnV0k9u0rr2F1ST1FJHbtK6zhY5WsmBlMTHHTJSWJU71y65CRRmJtMRqobl9tBIKpRXh+ivCHMwYYwX2+qpvzLEiq8YWRFE+WnBW0KzRCG/kgTj6Hc+D2Pb5ssMGmascAkaVjMEqphQ8KyQtiv5x5HjUrFoag+yQxGFUJRvaR9RGn0PsT6GWLkR4ciiiEwm3gSTRJOq5nCLCfdMzyc0zGZ/rmJ+ur6GQpN8wUjFJXUsfNgHbuM7c6DtewqqW8WOWC3mumen8KQHllMn9BL9xQWpNI5JwlvSKa0PkRZQ5iyuhAf76rnn1+Xx72E3pCMpmmoioocVZA0FZdFwqxpKLJKNBSl1hfC3yT6QiA4GrKi0RBSqA9GCYYVQsbveVhWCcsKsqbPFzRJ7yWqf0dVfMY2FhEUK3QXbfK99djMDMpLIsVpwRuW+fZgA5/vrDxMGFrNEgPyEhlUkMzgDkn0zknE0kYWgE+GSFTmmz01fLGllA1FFWwrrqG4vIF6bzB+jmQyYXfasXs8dMp30yk7id4d0+iem0RBqpOCVCcdUl0ktYF5z/fh8901rCiqOaX3PK8wlTFdUo/6utlsxuVyAbBgwQLGjh3LF198cVil5oMHD5KWlsYf//hHtm/fzsSJE7npppuOWKk5EonErz8RhFAUtEliRSPCURV/RCVolJaOKIpemdQwzFFZJao1n9Cp6EnoFpOEajKqm8p6/k8wqovJsJH7EzHCRGM5hLFJoqw0eh5jOQWgewlDEZlAWGnmSZQkcNks8fj5vjkJDO6QTOe01mtHoWka1Q0h9pY3sLe8nr1lDewq0T2Eu0rqKKlu3lMsPdFB19xkzu2bR3aqh+REJ263A7PNhi+qUtEQpqw+xOflYd7eVdJsYguN7UEyE+0M6pBMVqKdrMT2500UnF0oaqx4lB6+GZZVorJCVNGra0YUo8G9sbgU8xgqmoZJ0nOVQkaRi2DUCBON2ZCoQkjWdI+FbNgjIxoh3uDeqGQYVfTFqVBUaTY+i1kixW0lP8VJz6wEzumQTK9sD8lnIP8mHJXZXapHDuwsqaPoYF1cHJbVNLcPeekeuuUlc+W5hRTmJpOTnkByohOTzUqlN0JZfYiy+jDv7ajn/9aWU+mN6BEcmoYcVZBlBYumYjNJmDRdGPpDUeq8YaKK2uy9PA4LBekeehck0SEjj04ZHjpkeOggytkLDiEWWloXkPGHZUIRpYkXXyGixaIBFIKyMZ+IKgSiCmGZeA2BoBFiHvtdM0vQIdlBmttKOKpSVOXjv9+WHlEY9s9NZHCHZAYVJNErO6Hd9TUMRRV2V/j4akcFG3dXsf1ADfvK6qms9ePzhZpV8rU57CQlOunfIYPCvBQGdElnQKc0Oqa7KUh14j6LQmnbEsuWLWPhwoXMnTuXSZMmxY83rdS8d+9ennnmGRwOB1OmTGHUqFFHreDcEsS/qKBNEA//MkRh0Egyj+UDRWVNTzZXGyuRqkbiuaxpmEwSGvqELmzkHuhhocRXE0OyPmEMxSd0qhFqqo9B9yIqqKqemB6J6t4G+VBBZDWRkWinIMVJr2wPgwuS6Zeb2Co/Dv5QlL1lhhAsbzD2G9hX3sDesnq8wWiz8zOSneRnJNCvaxZjB7lwue2YbVZkk5m6kEpFQ4gNvgiaNwQ0eg0cVhPZSQ6yEu0M75IaF4FZxrGsRAdJTouoNCZoVZpGHsSKz4SjquHVU4jIxENIY9VJY6JQ1dR4GHowJgANOxE0+qsGZZVwVIvnLMUmlbKi24rYolJE1p83NR2SBG67ha7JemW+/nmJDOmQTG6S47SGnHsDEfaU1bOnrEHfltaz2wgr31/pRW0yyPREB4V5KZw/oICsVDdJiS6cTjuaxUJNQKa8IcTe+hBfbaknFK1t9AbKelhozBuoKirhsIw3EKbeH+GQuQrZKU7y09wM7pRCfrqbgnQPBeluCjLc5Ke5SW4DLToEbRdVM/IOAzK+kKxXM4+qBKMyEVUjoqpEFYWArBg5hWrcwx9W9MJU+oJRk9QRTSPVbSXVaSUs683ZV+8O6IVn1MbvusUk0Tc3kXM6JDGoIJneOW1fGEYVlbJ63Yu/s7SBbwwxWFzeQEW1D58/SDTcfK7gctlJS/HQrzCbHvkpnNMtk5E9M+mSmfCDDRMd0+XY3r/Txeeff84LL7zASy+9REJCAk6n87BKzWlpaXTr1o2UlBQAzjnnHIqKio5YqdlqbdkipBCKgjNOY2Nq3WCHoo2TuViV0JgolI1V+dhkTtE0NHSBGDIa4YaianziFpZVwvEJnZFbZOQtxqqRyqpeeEZRNCOfSCVi9EBqisNqIiPBTm6yg26ZbgbkJzI4P/mMrphFogrFld4mIrBREO4rb6CyPtjsfIfNQkaKi+QEJ7275WC12dAsFiJI+BUJWZPwA3tU2FOnQV0Ijz0aF3zds9IbRWCig0xjP8FxuAhUVY0aX5jyuiAbdlVSXheioi5IeV2QivoQFfX6/uonf3zG/l6CHxZ64Rnd0xcI65PCWCXDyCEhpFFVjdsQxVhkCipKfNIYMcReUG4SNtqkiIVGoyCMyrr9iMrG4tIhQshhNZOT7KRjqpOe2R7O6ZBCryzPaZlQqqpGSY2PvWUN7C6tj4vC3aV17C073EYkuGzkpnvokJNMv+65ON12zFYrUZOF+pBCpTfM114FrSGIovhRogqKouAwS1jRQNOIRmT8oSj1vshh3kC71UR+mpuumW4K0rPIT3fTId0dF4R5aS7sP9CJpuDkUVVNry4elPUK4yGFYFTWw70VhYiqRw75jUUi3WuoC8OgMc9o2vBeVTVcNhMpTguhqMqB2gCltY3CMPadTnJaGJCfRP+8RPrlJdI909NqUUJHIyqrlDeE9TDv+hD7q/xsOVDLrpI6DlR4qa7zEwlFCIfCKNHGMG6TSSIt2U3fThl0y0+hX+c0hnfPYkhhBk7hGWwTeL1eZs+ezbx580hOTgZg1KhRh1VqLigowO/3U1dXR2JiIlu3bmXKlCkoinJYpeaWIv4nCE4r8RV+OTaR08OvIkZIVjg2ETM8hVGl+WRO0fRzY6v7ESO0KxRVCEY0/QdC1sVlzEugqnrJ+rCs5x4pqr7KH22SjB5DAlx2MwWpDgpSXLogzEuib+7pXzXTNI2qhiDFFT6KK70UV3o5YGz1h4/yWn+z1XiTSSLJ48DtdpCQkkhCRgpRzGCxYLXbMFvMSJJEGLDazSQl2MlIsJPhsenbBDvpCTYyE+ykG8ddTX4QorJKtTdEZUOIyvoQG8rr4/u68NPFYEW9LgYPDT8FfZKcneIkM8lJl+yE0/o3FPxwiPUTDMm6NyFohJFGjCiCSKw9haKHo+thpKpeoVTVjFCzxnylQCR2zIhWUGKVkXWbFInqPdaUeCsM9TDPmNNmJjfZSX6Kkx5ZbgYatsN5CvufxULI91d42V/RwP4KfeFojyEK95Y3EI42r4SaluQkOdFFXm4qnTrZUC0WIpgIqhImsxlV1SiWFfZWy2iVERxm3ROoqSrhiIw/GKXBHzls8cxmMZGb6iI/zU1uqou8VBc5qS7y0lzkpuqCMD3R8YOuUig4dciKXnm03h/FG5LxGXOIoKIQkvXII1/U8BpG9G0oqn+vI9HGRaJYgTqHRcJu1nsUNwRlPQLAmDOAPh/okOpkUEES/Yzvcm6So1W925qmUeOPUlYforQ+RHl9iNL6MKV1QXaXNVBcUU9lXYBIMEw0HCESihANR5rdw2m3kJueQGGvLPp1SmNIt0z6dEyjU3YiljYmegXNWbRoEbW1tdxxxx3xY7NmzWLmzJnMnz+f3NxcLr/8cgDuu+8+ZsyYQTgcZsyYMfTs2ZNu3bqxcuVKpk6dGq/U3FJE1VPBKSNmjINNQr70aqT6BE8PBVXjYjBihI/GSsyHFb1QTWx1P6rE8gn0wjLRJqIy1vJC9xwoyHKsBYZeiObQ/9VWs0Si00pmgp0OqU56ZHoYmJ9IYebp61sUCEU5UOVrJv72V3jZU9bA/kovZTV+IofkK5nNJhwOGxabFcxmLDYrFpsVq92G1dhPcFgMwddcBMYEYOw1l02fENYFIlTVN4q/yoYglfUhqrzhw47X+iJH/CwWs0RmkpOsZCeZSY7G/WQHWclOspKcZCQ5yUp2kOBs/81uhU1pXeKiMKoRiMiGh0AlYoi9WNXQqKoRNbyDUVX3IgTlJlVGjUqmTfOR5SbCLyIrRgXjWG9VfdsUkxEymu6xkZ/ipHt8MenUhJsrikpprd8Qgl6KDTG4v8LLvvIGiiu98X5jMWxWM4keJ3anDZPVimIyI1msWGwWJJM5Hg5qN0lYTSBpGoqsEI7I+IJRfIeEpIOeF5ib6jZEnyEAU13xY3mpLlIT7O3+u90aCHtyYmia/vvfEJCpMcShP2KEkMp6n2JvRCEQUQjIekh4PCJANkLJDXGoqBpWE0RkPUQ19t1uGmqd5LDQOzeBPjmJ9MpOoE9uwhlvVxEIy828gWX1jfsltQGKK30EAroIPPTR9LPYrWbyMxPplptMn456Qamuucl0y0smLbF1xa6gfSM8ioKTQtX0sKtAVCUYbgz1iEQbRZ6sGLkChncwaoSGhIxJXEQ28gWM9hbhWHGaJhO5qKLphSeMSqRKE5F4qBg0GxVG0z1O8pOddM1w0zc3gT7ZCc28Zt/7s6u6J7Ck2kdJtZ/iSq++slfp42CVj7JaP9X1QfzBw0WXxWqJiz93ajJJNitWmxWny05WspusFCfpCXbS3DbSPDZSjW26IQaTnFbCEZkaX5hqb5gab5hqb4iKinq+26mLvirjEduXlSOvBaUm2ElPcJCR5KB3QTLpSdlkJDr0R1LjNi3RQbLLJrwEgtNC0/BRf1gmENZzjRpzAQ17YXgHw2pjdWK9EJWML7Y4ZVQfjSpqY6GaqBKPYIjZjiPZD7tF72OanWCnY5qLHkYLioIU50lPsjRNoyEQ4WCVbhtKqn3sr/BSVKqHhx6s9FJe5z9MnFqtFqx2KyaLBVtSIk6rFZPZhMlkBpMJu8WERdIFoBxV9FDQBh+hiHLYGKxmE1nJDrKSXWTnJ5Gd7CQrxdlMBOamukh0nXgVPIHgVCErKg1BmdpAlLqAjDcs449E8UZkfFE999AfUeJVioNGeypZ1eIRQ7KqVx+PykYrK1VDbZJDDOC0muibl0jf3ER6ZXvolZ1Auuf05cLKikq1P0JFQ5gKb5hKYxt/7g1TXh+i3t9U/EWRIxGQZaLhCIFD5hBWi4mOWYn06JFLYW4y3fJSKMxNojAvhdw0txCDgtOCEIqCY9K4sh+byCkEwzIhI1wrKhsCTzMqkqqaERLSZFVf1q/TQ770Ff2YCIyt6MuGl0BWtcPCQZpiNUskOiykue3kpTjonOaiV5aHPjkJJJ2CiY43EGZ3mZcdJXWN4q/aR3mNn+qGILXeEP5A+IhjM1ssWGwWLFYrFo+b7PQUkhOdZKW4yE33UJCeQGaSIy4Ck5wWTEhoqkowHKXWF2kUf/U+Nh3UxWB1Q5gaX5gab4j6wOGegBgeh4V0Q9wVpLsZ3DWN9AQH6UlNxZ+TjCQHqR471jaegC84u9CM0PJgVMUfkvFH9HD0kKx7CkOGhzCiKIQVBb9RuTBkFJfxhfWCMmElJgYVwlGFcDRWqVhtEmquHTEs2mYxkey0kp5gJz/ZQdcMN/1zE+mZ5Wlx3lxswehApR4lUFTawN4K3QNYVhOgqi5ArTd4WNQA6AtGZqsVs9WMKzkJyWTGajXjsFqwGeOQo4reOiMYRQ6GD7uH224hK8VJfrrLEH+uuAjMTtY9/tnJTlI8P+yG1YK2hazo84EqX5QqX4T6UARfRKYhouCLyPgiCv6wgi+sxgvTRY2IAMUoQCXLirGIbCz8HBJF5LGb6ZrloVdWAj0MUZiXfGq8apqm4Q3JVHqbCL+GcPPn3jDVPr3qrxyVkSNR5EgUJSpj1RRUWSYciuD1hYjKze1DdqqLzrlJdMpKpHN2Ep2zk+iUnUin7ERyUz3iuyw44wihKABiVUcbQzl8ITm+Qh+WFSOXUBeFIaVRCOqJ4noCeTCqNfEoao1hYsZKvtxkAqcYK4CHTuUkCdw2C8kJFrISHOSnOOmS4aJnlofCdHeLJnOxprvl9UF2lzewr8LLgSo/JVU+yuoCVNUFqfWFaPCH8QfChMJR1EMKMwCYTCbMNgs2mxW3x0VWVgopiU6ykl1kpbrJSXWT6rHjsJoxSxoYP1zBsEydP0KdP0KtL8KmbaWG4NMfDUcI/4rhtltIS7CTajw6Z3lIS9QFXlqCPf5aWoJ+LDXBfkq9pgLByaJp+iJSwGhc7QvJevinYQ9Chh0JRGWCspFrFFXxxfsaGvnFqkY4ogvJmA2JC0FjIelIiRNOq5nUBCsZCXbykpx0y3TTJyeBwgz3CeUdK4rKwZqA7vUr97KvwsvBah9lNQEq6wLUNASo94XwByKHlR4HfcHIbDFjMpuxulw4LWYsZj13WNPQvaARBRVQVYhGABSCIYWwVTYWdZxxr356oh7enR0XgC6yU5wknIH2GQLB9yWqqNT5o1Q0hKnyR6kJRqgL6x5DryEI9bmGGk9RiTSpHhyVFaMlhXbYArIEZCXa6ZHloWe2/h3vlukm7SSq5gYjCtX+CNW+CFW+MNXeCFW+iHEsrO/79GMRWZ8nKIoSF4EWTcWiqWiyTCQcwR8I0+A7PI8/yW2jICOBTh0z6WQIwc6GEOyYmYjLIb7XgraFmFn+gNAncJoeKhpV8AZ1MRgwqo6GjATxQLw8vLGSb5SGj4nGqAJhWdYb0sbCuYwiEE1F4BEW9OP5PklOKxkeG7lGZcBuGW46p7tIddsOyxnUNI1gRKHGH6UuGKDOH6G0NsCBKh/ltUEq6gNU1gepaQhS5wvj9YcIBCOEwlHkqHxE8Qdgtphx2K24nDay0xNJcNlIcNlJcttIdNtJcNhwOiyYJBORiIwvFI2Lvjp/hG93VVP3bSnBI4R7NSXJZSXZHRN2dgpzEuMCr1HwNRd+DpuoCihou+g9AvUQUF9IxhdS8IYV/FFZb01hhIwFZL3ptTei25FgRC8kEzFsSShi5AgaNkRuUope1Y4sBG0WEylOK2keG9mJDjqkOOMTxNxkZ7Pm1rKiUh+UqQ9G+XpvLXsrvByo8nKwyk95rZ/K+iC1DSHqfSF8xmJRJBLlsBUsAEnCbDZjtpgxm024PG6QJBQNooqGJJlAknRBCCiAooLHbo+HdGcmxTz8zmbh3bH9syG/Fxrz1cPR2L+3auzrz8PRRlEQjhUZijZWng0blajj4qFJIaLnbhjU2h9PcBQUVaUhEKWkIUJZQ4jqQITaUJT6kIw3onsSAxF9nhGJqnGvYEwYyk2iiQ6N2kl2WvX6AlkeuqS7KTTmDEdb/InPGwJRav0RagNRqryNoq/Gr4u+Kp/u/fOHG3/HNVVFjsoosoxd0rChgap7AiPhqC4C/WECoeaLvWaTRF66h44ZCRT0zKIgM4GCjMZHfoaHJLfoLXwmUNXG35hQRDGq6ysEI/o2duyiwSJn+HgIoXgWEftxDkUUfCE9VKshJOMPRwnIeh6gX9Yb0vojKn7ZaEwbVQ0h2BgGFj1KcrhqVBA7Wgkku8VEqtNKqsdGVoKd/GQnndKcdEhxkuyyxcM2mj6qvSGWflNPRZ1fD71saOrpixAIRQ2jraDI+vZoWCxmHHYLDruVxGS3vm+zYLNasJrNetiGJCGr4A/LeINRGgJRimujUBsF/Ee8r8dhIcVjJ9ltI9ltozAnkRSPzXhuHPfYSHHbSY4f1x9mU9sO8Yy1K5GVxvYk8f6VihbPN402yT1tbBtw+POokTsiK3pe6m0TClv7IwpOglj1T68htOqDqp47FI7iiypG3pCeH+iPKI2LSxF9EhjLRZZlNR5OHp8EHsWGSOjtFVKMvNysBDsFKQ7yk53kJDqwW0wEowr1wSiV9SFKagLsLa5mzaZiquqDeni2P4zPH8EfDBMJy8iyjCorR/T+gR4xYDKbQDIBJkxWO0gmXbBJJiSTFH/uspuNBR5H3NvffMFHX+jJTNYLPp3pEG+1SXuhRjGmxI9F5OM8jP6zzURaE6F3mLg7VMg1EYZHWihsKXaLCbvVhN1ixm41tfl+dT8UNE2PHiqpC7KvLkiZN0JNMEpdKEpDKJZPGBP8Rq6wrBe2U4w5RexxaBXyVLeNjmlOemZ56JzupnOaiw6pLhwWE96QTG1AF32ldSG2HGyg1hCCTQVhXSBCrT9KWFabjVlVVBRZxiZpOMxgkzQkVUGKKlhCEXyBMPWGDTkUq8VEjhFB1K9DMtkp7rgQzE9PoCDTQ06KG7OoIgrovx8h4/9AOKLodiGqxgVb2HBQ6LajyX4TQReKKoSN35aw8by56JMN0ac2f27c+0Ro+M8Np/kv0f4RQrGdoLeZ0EVgfUimIShTH4riC8k0RBV8YRlfJIrfKPnuj8oEw0YD+qheRCbaZOU+NmFrunJ/NAGoaRpmScJmNeF2WEiwW0hwWPDYzbitFhxWE6qi0hCMUusNUeMNUVzdwGZ/BF8gQiAUIRwxhJ6ioDbdHmMCB2C1mLFY9Pwdp8OCyWQHCTRN0kPTZBVZpdlqfhgIR6E+qoE/CkRx2y0kuqwkumwkOK2kJljpnJWgH3PaSHTZmgs/Q/SleGwkuWxHnezF/l1ioihWYj9WsbU+GDim2Gq2VRtbeMTFlnqE844i2KIxcSc3EXlN7hG75tB7yKdiRncMhFBse+j9RMEfiuoTrECUumBUtykRlfpw1BCAUfxho+G8rNufiKLEFwJi9qMxiuBwGxL7fkvoky2PzWzYDjMuuwW7JKEqMsGQjDcQoc4XpKyilh1+feIWDEeJRvXV/LgNUZQje/1iSFITsSchWfRQtLjoM0kkumJiz9EY5h0XgbZGr3+iPR7efWjbC1lpLrIicqOYKmsIs78mGBdTxxJnTZ/HhV0TIXa8a2KPU/ldtplNcXEWF2tN9t12C6kek3FMf81maf7cbjUZ9zFet5oaz4udY+w3Xqvf32qWzgrvantGUVWqvWH21oTYVxekwh+mNqiLQZ/hHYxNzGM2QY4LQT2l5VDPoARG0Tl9ISjdbcNtM2MzmzBJEoGITH1QZk+5n6/31DURf9FmYZya0c5FkRWshvCzAmZNQVVULFEZJRIlEIzgDYSp94WRj5haIpGZrBd26tIxlZw0T1wQ5qQ1btMSnG0+PzBWU6KpEAsZbX6aCrFQtFG0xVKE9OiO5q+FjWtDETX+WlOBd6zXjpQj3lJMkoTTZsZuM+OwmnEYW7vVjNNmxuOwkp7owGlteo4+J9XPsWCPXRd7vel9ROTWCdGuhOJjjz3Gxo0bkSSJ+++/n/79+7f2kL4XmlHWPRCWqQvK1PllakL6ylh9KIo3ouILR/GGZb3lRFSJVxaNGu0i9L6BRriGhiH+9MTuWA5PUwHYTAxqmj550vS8OklTURUFWVaQowqRqGyEYukTM0XRX1cV1ZisqWix7XG6rEiGiEOS0JD0XwuaTOCMiR3xyZwESJhMEna7BVeTh9PwEjptRvEHmwmbRTceVosZq8WkC0yzhMVixmwyoWhNyuKrjZ6wiKJSHtY4GAgRrQg285DFRFSjwFIPea5ffyawmiUsZhMWk4TNom8tZhMWs4TN2FrMpvi+02Ym0WzCGnstfp3+3Go2xe9pNZ5bjvDcGruHSdL/rmYpfg/b8a4xS1jMbfeH9Wy0J4qqEggpVPojVAX0nJraYJTakKyHf4V0L2Awaqz2Ko05QE2Fnxq3K4adUo9sQ/TJmoamqKiKjCqrqLKCHJX1yISoTCQiI8u6DVFlBVU17IiqT/SOGp4QR4ovAukC0IzJasFkMuGwWXA7bXiMR6LbjsdpxeWw6kLEmAzYrGb9/6kRNmoySfHvb9PwtzpZpbI6TKQiSESuPaqAiz1OlS6TJF2Y2SzNH/Zm+7o4a3bOEa459PVDBdpxrzHOESKtZZyIPYl9vyRo9b+vqur/h8sbQuypCXGgIUh5Q4TqYFS3E+EowWijh1luklqiNg0RVwGazzXMkoTNImE3vnMmSf/soaheqKbBG+FgZQBoIvgU3XagGW1dJA2zphrzEwVTVEGOyIQiUfzBCN5AhKh85N9fu9VMZrKLzBQXHfOTyUh2kZnsJCPJRYaR55uR7CIjyUl6kvOEewnGCnLFxFisyE44atRwiDbmVcb3j3A81ponHI0V6mms53Bixxtb+0TkWNugE/eiHQ+zScJhNWOzmuLCzGYx4zDmWg6rmUSXFbvVoZ93yGv2JtfqrxmLQjYzdkvz++qP2MKRvq+LPX0O19rfk7bA7NmzWb9+PbIsc/PNN9OvXz/uvvtuFEUhIyODJ598EpvNxrnnnkvnzp3j182bN49ly5YxZ84csrOzARg1ahS33HJLi96/3QjFr776in379jF//nyKioq47777WLBgQWsPKz45C0YUav0RKvwRqn1RKgNhagOKHqZllHYORY2wLGOlOFacQTYMr9JkBa6ply8WoqEoihF+qaDIqv5QYiJOi4s4VVX11X1FRdVUMCZ9Wuy4agg79fgCrzmGwGs6acOEZLHEn5tMEiazCbPJjGQy6UUdTCYkkymeu6PFRKIhCmNC8bDnxupd7Do/4FeAABCQARk4PESkKWaTLpKaCpeY8LEcIpYsZhNWk4Tdbm48z3zo9fo5liai6Egiqtm1FlOT94wJrKOLvMPElkkYy1NNW7UnoBdICEYVav1RSr1hyhvCVPqj1AYjetNpoxVEICI35nbJMQ+xqpeFNxaKYqHiergXKLJMNKLEPXOyrKAaoaGNi0G6rWi0JWpc1GmGzYhtjy/ymiA1Cj5JMmE2mzHZTEbopxmTWUIy6fYCkwnNpLeDMBmiJW4fTBImyWToR/17EQACKlR4VfCGOZZdaPp9tRnfTZvZ1OyYxRBTHrulmVg7koA7kshqfq75uNeJ73j75kTtybZSP3V4kZAwmcBsljBLEmaTCbNJz+GP2XyzpIdFSxLxvP3Yf5HY77beFkLfKipEjeJQ3lCUqkCUsoYwFX49LLQhLOMP6YvOwahMNKrGFz9jUUZavHVMbP7R+F76lEFtIgg1fWFIbWInDJuhKiomNMxomDQNNBWM47KiL0RHjb6ekah8TDNiNkk47FZsVjM2q55GkpToIS3VjMWi2xGz2YTFYtH/jhYTIKFqujgtj6iUlIVRS0Ioak2zxTBFbQyFbRZl1ewcVZ+PGfOzU42E7tU0mSRMsTlU031jazZJemE9k6T/v7GYcdmsJJgk/fPH5yWNcwqLJbaIa9bnExZjbmF47G1Ws/E8Zov0v6nNbLyPMX8yN5nbxN7HbJKwmqX4eVbjWHwc8esOP7/pvWLXC/t3ZFavXs3OnTuZP38+tbW1XHHFFYwcOZJp06YxefJkZs+ezcKFC5k6dSqZmZm89tprza4PBAJcd9113HTTTSc9hnYjFFetWsWECRMAKCwspKGhAZ/Ph8fjiZ+zuqiGhForEUUvmhBSFcIRlWAkqodmhjWCYT1cMxCO4jfimsPGhEtWFKJGgrWi6K5zRdaNRNwgxlbX48ZUi6+qA8ZxVRdEqoaG7q2LnYeGfiz+PLY63+QYHHb85GkUdzFxpnvtYgJPF3ExT15M1JnNJixmM5a4l86s9/YyS9jiXjtTkxUh3c1vs5qOL6yOJL6aeqRMzY8fJr6OcE5Tb1ZTQ9XWQ0UErcOJ2BOAL7dX4ak266E5Rj8+f0SmIRg1qnpGCYRkfCHZqBis5wBH5caVX9lY1JGNVXMlNpmKLQgZpd1VramtiE3G1Ga25jC7oek2Ji7amtiX5vvfw4Y0EXgYIs9kNRu2Q7clJkmf5VqtFixmM1arCZvVit1uwWm34mwSCRBbqT6aSIstktgsjd9tm2FvbLHvu+WQ6+L7jdcddi+LbjeETRCcak7UnnxRVI6nFlQAFRT9a4qGvlAckRUCIb0aqD8UxhfUq2eHIlG9DYysEI0qxoJQrPCLGp+TqLH5R+wrH/P4E7MjAMZ8xbAdxMRgfM6hNl7b1N6oTezPMeO+j4Ax/2i+yKynjEhWu74IhNTE1pji+wARSSKiARHQd2ILxcd/28aFaH3fdOh+XIzpwtxmMfKWJTAb9iIu1KSYYDv0Wil+P32fZsekpueYMBYK9NdoYo6amumm+6qm53LGUl1Urfl+zLGgqHqrMk0BJaqharLx2xKJ946NpwrE9o+ScnSmiS+kN7Pjx/4diNv4Jot0R/x9aPa8+W/EmB4Zrf3Rj8nQoUPj0QlJSUkEg0HWrFnDH//4RwDGjx/PvHnzuOyyy1CUw+t3+P1HrrvREtqNUKyqqqJPnz7x52lpaVRWVjYzxDf9zz9QzE0Ns3bE3TjSYTtNnjY5FjdyseNN9uPbRkN47H0gVtxEkozLG+8ZN5AYq4gxg2qKGU/deOnhU7EVIz2Hz261YLfpsdsupw2P3UKC04rHYTVy8awkuGx4HBYSHFYSnBYSnTaSXPo22W3FabNgFhMpwVnOidgTgJ8/+HoTm3Ky9uREbYnUeE4zu2E6si1pcr7UNJxbQr/G1MSGNPHaxyZiku6qwCzpq/CxBSG7kefhdFhx2S24HRbcNgsuhwW33RK3Hx6nRbcpDguJLhuJTgseh5UEp5Ukl5UEh1WIMsEPghO1J0//9b3mc5QTsSPNPC2H2ItD7cmRbMSR7McRkZq/tyGo4tE/Rg4hccGkF4HSF2zNxsKxvhjkcljwOG3xxaGmXnp9om5u4sVqzFmNeeCtRp5qLNfMZjHCGI19q+E5s1oaBYSliRCwmsWCUEuILU7GhaPhpVZjKTtqY056VIn1s2xMV4i9HvNONz1fVhvTdpper+/Hajyo8fvFomNi26hxrOnxQFiOH4+lDRx6/onmSO7+08Un/Hf6eEcVH22rPNk/8xG5sGcG47unH/V1s9mMy+UCYMGCBYwdO5YvvvgCm03vG56RkUFlZSWBQIDq6mp++9vfUlFRwcUXX8wNN9xAIBBgxYoVfPbZZ2iaxj333EPPnj1bNMZ2IxQPDZHUjPy6pkg2O5LVGXvWxB422Y8Jr/grsa0Wt5MmScJkhHtIkpFP0LhE15i/Ey/prByxAfuRMEkSToeVBJcdj9tBgsuO22XH7dS3LqcNl8OG02HH6bRit9mw221YbBZkBb3qU7R5YnLYqDQYqx4ViuhfpIr6MP6wjD987NCOpjhtZpJdNhJdVpKN4i9JLitJxjbZbSPR2XRrJdllIy3B3uKG1QJBa3Ei9gRAsjniNkVqMsk6mj2J7cfsicmwPSbjEXtNikcixBrEG7nBJ5jzardacMfauSQ4SfI4SPQ4SXTbSfQ4SXA78LjsuF361uWyYzabiSha3G5EZLW5LYkXQdC3IVkhGFbwh2WqGxptyYnaE0mCRKe+QJVk2JQkp75olWQUkYrZlhSPjTSPLV5B1OOwiFAkQbvhhO2J3YVkcccFWLPX4ufHij9p8ae64ymW26jPRyQj4kBVFWMuohz3eylJ4HTY8LjseJx2kjwOkhKcJHqceNwO3C4HTqcNu92OzW7FbLESUTR8oSgNRoXwhmDz/WBEifv3ggoQ1CCoVxFPdFpJS7CTnmAnLcFKgttOToqTnBQnuSlOclJc5IqeoK1OzONqQmo/ouA4xARoOCYgZSMn/ZBiYO2FZcuWsXDhQubOncukSZPix2O2x+l0cvvtt3PZZZcRjUaZPn06gwcPZsSIEfTv358RI0awbt067rrrLt5///0WvXe7+T+RlZVFVVVV/HlFRQXp6c1V+NbXbiE/v3V6osiygj8UxRsI4wvoVbb8RrWt2HNvIIwvVoHLG6LWq/f+q6puYKc3SHV94KgTRUmSSEt0kpnqITPFbWw95Ka6yUrx6M9TPeSkJZCV6tbzfAxUVe93FgjrIXL+kN4T0B97HpYJhGWjDH6Eer/+A1Dnj1BWF2TbwXrqjR+FY+Fx6E3i0zx2Uj22eGP42LHGff01t11MBgWtw4nYE4Ctr/36jNoUTdPD472BCF5/mHp/6JCt3ie0wa/38ao17EZ1nZ+d+yqprg8QPUr7GKfdSk56AjnpCWSnJZCTpu93TUsgJz0xfjzRbT/m9zLWnywQUfCHdNsRE5C+kGxMIiNxm1EfaHy+t9JPQ1Df94WOHjpms5hINYRjzJYcup+RaCcryUlmooMEp7AlgtbjRO3Jqr/9lB5dO52WlimaphGOyPhDUfyhCA2+MHXeILXeIHXGfKPOG6TOF6LOG6LOG6SqPsDeA1VU1PoO6wkYIy3JRV5GIvmZieRnJjE4P4n8zDT9eVYSGckewopGnT9ClTdMtTd8hG2IvZU+1u6qptp7eP6wx2GJi8fsZCcFaW46ZhiPdDeZSQ7x/Ra0CD330XxKHRjju6cf0/t3uvj888954YUXeOmll0hISMDpdBIKhXA4HJSXl5OZmYnH4+Hqq68GwGazMXLkSLZv385PfvKT+H2GDBlCTU0NiqJgNp/436XdCMXRo0fz3HPPce2117Jly5b4H6atYLGYSfKYSfI4TvoemqbhD0ao8QapbdAf1Q0BquoCVNT4qKj1UV7jp6LWx9otB6io9eMPRg4fi9lETnoCeRmJ5GUm6duMRPIy9W1uRiI9clOxWlr2BVJUDW8wSr0x6Ytta/0Rqo0fhGpvmGpfmLK6EN8V11PtCx911cZpM5OV5CA72UlmkoOsJIexdZKV7IgfS3HbxI+E4JTSVu2JJEm4HHpUQVZqy8ejaRreQJjqet1uVNfrD912+Cit8lJa5WXjjlIWV+84ov3wOG10zEmmQ1YyHWLb7GQ6ZuvbzBR3vAJxesLJN4+WFRVvSKberze/rvaGqfGFqfE17uvbCJv21VHjC1MXOPJk1mkzk5nkIDPR0cyWHGpX0hLsIrRecMo5UXuS7D56q6XviyTpRV8cditpSS7Iatn1vkCYilo/5cZco8KYa5RX+zhYWc+BigZWby6mpiF42LVZqR4KspLokpeqP3JTGVqQQte8fLLTPM1+v8NRhbK6EKW1AcrqQpTUBiitDVJSG6S0Nsj2knLK60PNvKNOm5kO6Y3CsWOGm06ZHrplJ5Cf5hbfacFZi9frZfbs2cybN4/k5GRAr1y6ZMkSLrvsMpYuXcqYMWPYvn07c+fOZdasWSiKwtdff81FF13E888/T2FhIZMmTWLHjh2kpqa2SCRCOxKKgwcPpk+fPlx77bVIksRDDz3U2kM65UiSpIeEuOx0yEo+oWv8wQiVtX7KDYNeWu2lpLKBg5UNHKyo59udpSz6cjvBcPMJliRJ5GYk0CknxXgkx/c75qSQm55wWONYs0mKN5E/UTRN00PXmq4wGhPByvoQ5fUhyuuDbN5fx8f1Ifzhw70MNouJjEQH2cm6qMxNcZKbqoes5KW6yEt1kZnkED8WghPmbLUnkiSR6HaQ6HbQOTf1uOd7A2FKq7yUVXspqWq0HfvL6thfVseqTfup84WaXeO0WynISqJjdjJd8lMpzE+LPzrmJJ/wApTFbCLFbSPFbaPTCX4+WVGpNTwXVQ0hKurDlNcHKa8Pxe3JjpIGvthWQf0RRKXFLJGV5CQv1Uluiovc2DbFSW6qbk/SPHaR3yRoEWeDPYnNPbrkHdtu+IMRDlY2cKCingPl9fq2ooG9pbWs+a6YhZ9sbpaK43JY6ZybQufcVLrmp9K9IJ2enTLo2SmDEd2PXEgkHFU4UB1gb6WPfVV+9lX62V/pZ2+lny+2VRAIN0ZNOKwmumQlUJitP7rnJFKYnUDX7AScok+eoJ2zaNEiamtrueOOO+LHZs2axcyZM5k/fz65ublcfvnlWK1WkpOTufrqqzGZTIwbN47+/fuTmprKfffdx2uvvYYsyzz66KMtHoOktaw/QpvkwIEDjB8/no8//rjVQk/bMpqmUecLcbCigYOV9ZRUejlQUc++sjr2ltayt7SWkkpvszwLq8VMQVZSXEB2zU+jW4E+GeySl4rddnrWGPwhOT7xK68PUVEforwuGN8vqwtxsCZwmKA8dAKYl6qLyTxDUBaku0l2WYVnUnBCCJvSSL0vpAvH8jr2l9Wzr6xW35bWsutgDfVNhKTFbKJjTjKF+Wl0jQvIVLp3zKBDVtIZ/f4FIwoV9SEqGxoXpMoMz8XBmkDcg3FoxIPNYornUeWluMhPc9Eh3U2HdH2bm+I6bV4hwdnJD8meRKIy+8vq2V1Sw+6D+mNPSW18PxRp/O3OTHHTo2MGPTtm0KNTBj07ptOjYwZ5GYlHtRWaplHlDbOnwkdRqZedZV52ljZQVOZlf5U/3ttUkiA/1UWv/CT65CfRKz+ZPvlJdMr0iEVlgaAFtBuPouDkkSSJlAQnKQlO+nY9cjxKOCJzoKLeEI517C2pjYvI9z/fRmVdY4ldk0miQ1ayLhwL0uhWkE63Al1I5mcmHeaJbAluh4UujgS6ZCUc9RxN02gIRimpCXKwNsDB6kB88newJsCGvTUs2nD4BDDBaaFjuoeOGW4K0l10TPfQId1FxwwP+WkuHKIYj0BwGEkeB/0Ks+lXmH3Ya5qmUVUXoOhANbsOVFN0oJqiAzUUFVfxxcZ9zUJbPU4bPTpl0LtTJr06Z9CrUya9OmeeNgHptJnjeU5HIzbpLKkNUlIT4GBNsDEUribAmqIq3lkbbFZBzyRBbmpMPDYKyNgjM9EhPJKCHyw2q4VCY25wKKqqsr+snq17K9i2r5Lt+6rYtq+SBR9vaha5kNzE5vQ3tr07ZeCw64u9GYkOMhIdDCtsni8WiirsKffFxePOUi9bD9bz8aay+HfYaTPTMy+RPvnJ9C5I0rf5SaKgjkBwFIRHUXBC1HmD+gTwQDVFxVXsLK6mqLiancXVeAONyel2m4Vu+Wn06pxJ784Z9O6cRe/OmXTOTfleArKlqKo+AYyJxwPVAfZV+dlfpYewFFf7CUWbC8mcZKc+6cswBGS6hy5Z+iPVc/K5WIL2h7Ap3x9N0yir9rHrQDXb9lWydW8lW/dUsHVPBWU1vvh5hwrIAd1y6F+YTXry0QXemURWVA7WBCmO2Y/Yo9rP/qoAFfXNQ3MdVhMF6W66ZHronOWha1YCnTM9dMn0kJPiFFENP0CEPTk2mqZRXuNj+75Ktu2r4rtd5WwsKuW73RXxxSaz2UT3gjT6d8uhX9csBnTLYXDPXFITXce9fyiqsKOkge8O1LOluI4tB+rZcqCeWr9+b0mCwqwEBnRKYWCnVAZ1SqFPh2SxeCwQIISi4HsSM/Ax0bhjfxXb91Xy3Z4K9pfVxc9z2Cz06JhOr06Z9O6caQjJTDrlJDer0HqmUFWNioYQ+2P5D7FttS4kS+uCzZLpU9w2fbIXm/hleeia5aFLZgJuh3DMn20Im3J6qWkINArHowjI3IxEBnbLpr8hHAd0y2k1e3EsghGF4urmInJfhZ/dFT72VvgIN4lscNrMdM700DlTtx+dsxLoYtiV9IRjV5sVtF+EPTk5VFVl98Favi0q49uiUjYVlfFtUTkHKurj53TJS+WcnnkM6ZXHOb3yGNgtB7fz+HUUNE2jtDbIdwfq2bS/lm/26o/Ywo/FLNErL4mBMfHYOYUeuUkibFXwg0MIRcFpwxcIxyeBW/bqE8EteyqbGXmXw0rfrlkMKMyhfzd9MtinSyYux4kXzDkdhKMKxdUBdpd72VPhY1e5j93lXnaX+yipbV71LSvJ0UxAFmYl0CM3kYJ0UY2tvSJsSutQXR/g252lbCwq49ud+uRw274qFKNtUKLbHg9HG9wjlyG98uneIa3NiccYqqpxsDbAnnIfuysabcieCr1Ih6w0/vwmOq10z02gW3Yi3XMT6ZajF+bIT3WJUNZ2jrAnp5aahgAbd5SybttB1m8rYf3Wg/F5hckk0btTZlw4Du9TQO/OmScU0RQTjxv21vLN3hq+2VvLxr218dZgHoeFIV3SGFqYxvDCdAZ1ScVtFwvFgrMbIRQFZ5wGfyguIDfvLjcmhGXxohgmk0T3gvS4cBzQLZv+hTlkpLSNULRAWGZvpSEey7zGBNDHrnIvNb7GnCyH1URhdiLdcxLolqNP/rrnJNApwyOKYbRxhE1pOwTDUbbsqeDbnWVsLCrl251lbCoqw2eEpCW67XHROKR3HkN65ZOXkdjKoz4+sqI2W4zS86r03KrKhsZwfqfNTLfsBF08xrY5iXTKcGM5g+H8gpNH2JPTT1m1l/XbDrJ+60HWbzvIuq0H4608kjwOhvfJZ2S/Dozs14EhvfJPyOsI+mLPngofX++pYe2uar4qqmJ7SQOapleC71uQzLDCNIZ1S2dY1zSykp2n82MKBGccIRQFbQJN09hfVsfGnWVs3FnKt0X6tri80fuYk55gTAjzOKenvlp4IvkJZ5I6f4RdZV62lzawo7SBHSVedpQ2cKA6ED/HapbokqV7C7rnxraJdM1KwCYEZJtA2JS2jaqq7NhfzbqtB1i39SBrtx5gU1E5UVkvm5+TnqALx565DO2dz9De+Xhc7SfPuNYfYWdpAztK9IIcOwx7crCmMZrBZjHROdNDr7wkeucn0Stf3+aKPMg2h7AnZx5N09hTUsvqTftZtXk/qzbt57vdFYBenXlA9xxG9u3AqP4dGNW/Y4v61tYHIqzbVc1XRbpw/GZvLcGIbns6Z3oY3SODc3tmMrpnBhmJJ99bWyBoCwihKGjT1DQE2FRUxsadZXyzo5Svtx9kx/7qeCuPLnmpzYTjoO45rR62eiT8IZmdZY3CcWdpAztKveyt9MVzIa1miW45ifFJX5/8JHrnJ5ORKHKXzjTCprQ/QuEo3xaVsW7rQeNxgJ3F1YBeCGNAYTYj+ukTw5H9OpCb3va9jofiC0UNr2OjeNx2sIH9VY1VqZNcVl00GgKyd34SPXKTRC51KyLsSdug1htkzeZiVn67j1Wb9rNu68F4u45enTIYO7gz5w3qzNhBnUlLOvFF6Iissnl/LV8VVbNqRyUrd1TiDer37ZGbGBeOI3tkkNKCPtQCAcDs2bNZv349sixz8803069fP+6++24URSEjI4Mnn3wSm83GueeeS+fOnePXzZs3D1VVuffeeykpKcFsNvP4449TUFDQovcXQlHQ7qj3hdiwvYR1RnhJ0/wEs9lE704ZujehVx4j+nWgZ8f0NpvDFIwo7C73sr2kga0H9UpsWw/UN8uDTEuw6xO+vEavQffcRFGR7TQibMrZQa03yNotB1i1SfcofPXdAYJhPd+oU05K3Jswso3biePhDUbZZtiPLQd1G7LlQD2+UGPPuk4Z7iaex2T6dUimIM0lFqHOAMKetE0iUZkNO0r54pu9fLZhD19+ux9/MIIkSfTrmsV5gztz3uDOnDugE0meE/cMyorKpv11fLGtgpXbK1m9s4pgREGSoG9BMqN7ZjCuTzbDu6WL33HBMVm9ejUvv/wyL774IrW1tVxxxRWMHDmSsWPHMnnyZGbPnk1+fj5Tp07lJz/5CW+99Vaz699++22+/fZbHnroIVasWMHbb7/Nn//85xaNQQhFwVlBLD8hJhzXbT1IrVcXWykJTob3yWdEPD8hr016HZtS648Yk73GUt7bSxri4S1mk0SXLA998pPp1zGZ/h2S6dchhWSxWnlKEDbl7CQqK3yzo5RVm/azctN+Vm/aT7lRaTUlwcnoAR0ZO6gT5w/uQp8ume1WOIIeeldcHYiLxi0H6tl6sJ7d5d54U/IUt41+HZLp3zElvu2U4Rbi8RQj7En7ICorrNt6kBVf72bF13tYtbmYcETGZJIY1COXCUO7MmFYIcP7FGC1nLjAi8gqG/bU8OX2Cr7YVsm6XdVEZBWnzcyoHrpovKBvFp0zPeK7J2iGoiiEw2FcLheqqjJy5EjcbjeLFy/GZrOxfv165s2bx6xZs5g2bRrvvvtus+vvvvtuLr/8ckaNGoWiKJx//vl8/vnnLRqDiEURnBVkpyVwyeieXDK6J6BPknYdqGHVpv2sNvITFq/eCTTmJ4zoWxBPbm9rYWgpbhujemQwqkdG/Jiiauyt8LGliedx7a5q3llbHD+nQ7qbfh2SGWBM/Pp1TCE9of3kZgkEpxOrxRzPWfztlFFomsbugzWs/HY/K7/dx2ff7OW/X2wDIC3JxZiBnXSvwqDO9OyU0a4mcZIk0SHdTYd0N5MG5saPB8Iy20oa2LSvlk376/h2Xy1//2gHUaMCa6LTSt8Oyc3sSJesBFHBWXDWY7WY43OCe288n1A4yldbDrDi6z18un43c/75BU/84zMS3XbOP6cLFw4r5MJhhXTMSTnmfW0WE8O7pTO8Wzq/vxT8YZmV2yv5dHMZn35XzsebvgGgY4Y7LhpH98gU4eJtjA+/K+eDTeWn9J6X9Mticp+so75uNptxufQw6AULFjB27Fi++OILbDbdKZCRkUFlZSWBQIDq6mp++9vfUlFRwcUXX8wNN9xAVVUVqamp8XuZTCYikUj8+hNB/C8UnJVIkkRhQRqFBWlcf/EgQM93XLO5mNWbi1m1aT9z31vP8wtWA9AhO5lzB3Rk7KDOjB3UiU45KW1uUmg2SXTNTqBrdgI/OqdxVbrGF45P+L7dV8em/bV88PXB+Ot5qU76dUihf0fd69i/Q7KozCYQoNuJrvlpdM1vtBPF5fV8tmEPy9fvZsWGPbyzYgsAWakexgzsxPnndOH8wZ3pkpfa5mzEieCyWxjcOZXBnVPjxyKyyvaSer7dV8e3+2vZtK+OV5fvIhRVjWvM9CvQPY6DO6cyuEsqHdKF51FwduOwW405QWce+PkF1HmDLP96Dx+t2clHXxXx3mdbAejeIZ0JwwqZOLyQMQM7HTdiyW23cGH/HC7snwPAngofn35Xxqeby5m/ci/zlu/CapYY1SODiQNymTQgl/y0tlW4T3BmWbZsGQsXLmTu3LlMmjQpfjwWFOp0Orn99tu57LLLiEajTJ8+ncGDB3No0KimaS222yL0VPCDJSorbNxZxqpN+1i1qZgvvtlLZZ1eFCI/M4mxgzoxZmAnxg7qTOfcticcj0V9IMKm/XVxAblpfx27yr3xwjnZyQ4Gd05lkPEY2CkFj8PauoNuQwibIgD9R3VvaS0rvt6jPzbsobTKC+g5jhcOK2TCsELOP6czie6zq7phVFYpKvPy7X59AerbfbVsLq6Lh7+nemxxGxLbitD3IyPsydmHpmls31fFR1/tZNlXRXy2YS+hiIzDZmH80K5cPKoHk0f1ICc9oUX3DUcV1hZVs2xzKcu+LaOoTLc3fQuSmTggh0kDc+nfIbldzUcE34/PP/+cZ555hpdeeonk5GTGjx/PBx98gMPh4KuvvuL111/n2WefbXbN7Nmz6dq1K2vXruWSSy5hzJgxRKNRLrjgghaHngqhKBAYxAz/Zxv28Nk3e/l8wx4qanXhmJeZyNiBurexPQpH0Csmbt5fx7f76/hmTw0b9tayp0LPz5Ik6J6TGPcWDOqUSs+8xB9snzZhUwRHQtM0dhZX8+n63Sz7qojl63fjC0awmE0M71tg5DB1Y3CPnHad33g0orLK9pIGvt5Toz9217CzrCG+ANU1yxNffBrcOZU+Bcmi5Q/CnvwQCIajfPHNXj5ctYMPvtzO/rI6AM7plcclo3pwybk96dc1q8XzhqIyL0s2lrD0mxLW7qpG1SAn2cnEgTlMGpDL6B4Z2EVBnLMWr9fLtGnTmDdvHmlpaQA88MADDBkyhMsuu4xHHnmEHj160L9/f+bOncusWbNQFIXp06dz//33s2/fPlavXs2jjz7K0qVLWbp0KXPmzGnRGE6ZUFyyZAlz5swhOzsbgFGjRnHLLbewbds2/vCHPwDQo0cP/vjHPwLw0ksvsXjxYiRJ4je/+Q3nnXceXq+XO++8E6/Xi8vl4qmnniI5OZmVK1fypz/9CbPZzNixY7ntttuavbcwwoLTwfGE4wXndGX8sK5ccE5XMlLcrTzak6PGF2bDnho27Knl6z01fLO3hhqf3sjcaTMzoGMKAzunMLhzGoM7p5KXeuZ6tAmbImjrRKIyazYX89FXRSz7qogNO0oBPb/xgiFd9Rym4YVkp7XMq9Ce8AajbNxb2yge99RQUR8C9NysvgXJDO2axtDCNIYVppOZ1DqeV2FPBGcKTdP4bncFH3y5jQ++3M7aLQcAKMhK4uLRPbh0dE/OG9y5RQVxAKq8YT7eVMqSb0r49LtyghEFj8PCxP45/GhIPuf3ycZpE6LxbGL+/Pk899xzzdpezJo1i5kzZxIOh8nNzeXxxx/HarXy+OOPs379ekwmE+PGjeOWW25BURRmzpzJ3r17sdlszJo1i5ycnBaN4ZQJxbfffpv6+npuuummZsevv/567rrrLvr378/tt9/OlVdeSZcuXbj99tv5z3/+g8/n49prr+XDDz/kb3/7Gw6Hg1/84hf885//pKSkhLvuuouLL76Yl19+maysLKZNm8YjjzxCYWFh/D2EERacCZoKx+Vf6zlMscqqA7plc8EQvSLaqH4dcNjbZxinpmnsq/Tz9Z4aNhiTvs376wjLeq5STrKTIV3TGFaoT/z65CdjPU0eA2FTBO2NilofH6/dxbKvivh47a54RdUhvfK4eFQPLh7dg/6F2e0uGqElaJrGwZogG/bqHsevd1fzzd7auA3plOFmaGG6bkO6ptE9JxHTGSiUI+yJoLUoq/ay2PA0frx2F8FwlGSPg0vP7ckV5/dh/NCu2G0tKxkSiip8sa2CRV8f5MMNJdT6I/Hcxx8NyWdcnyxcdlGGRPD9OWX/i/x+/2HHIpEIBw8epH///gCMHz+eVatWUVlZyZgxY7DZbKSmppKXl0dRURGrVq3iscceA2DChAnccsstFBcXk5SUFFfA5513HqtWrWpmhAWCM4EkSfTslEHPThn86ophKIrKhh2lfLxWnxT+ZcFqnv73lzhsFkYP6MgFQ7oyfmhX+nXNajdhaJIk0SnTQ6dMD1cO7wDohS62HKhj/e4a1u2q5quiat5fr6+QOm1mBnVOZZjhMRjSNY0k16nJUxI2RdDeyEzxMHXiAKZOHICmaXxbVMaHK7ezaOUOHp77Kf/78ifkZSZy8cgeTB7VnfPP6YKznS4qHQ1JkshPc5Gf5ooX3QpHFTbtr+OroirWFlXzyeYyFqzaB0Cyy8o5XdMY1jWNYd3SGdgp9bR4RYQ9EbQW2WkJ3HTpOdx06TkEw1E+XruLd1Zs4f0vtvH64m9IcNm5eHQPLj+vNxOHF55Q+y6H1cyEfjlM6JfDE9eprNxRyfvrDrBow0HeWVuMy66/fuk5+Yzvl41biEbBSXLK/ucEAgFWrFjBZ599hqZp3HPPPaSkpJCY2Nh2IFbGNTk5OV6uFSA9PZ3KyspmZVzT09OpqKigsrLysHOLixvbAQgErYXZbGJIrzyG9MrjnhvOwxcI88XGfXy8dhefrNvF//xtKf/zN8hMcTN+aCEXjezOhGFdSU1sX9XLbBYTAzulMrBTKj+/QJ/8lNQE+GpXdVw4Prd4O4qqIUnQIzdRDzXrqnsNOmV6Tup9hU0RtGckSWJAtxwGdMvh3hvPp7zGx5JVO/hg5Xb+tXQjL767FqfdygVDupx04Yv2gt1qZkhXfSGJSbrXcU+Fj6+Kqlm7q5qviqr4eFMZAFazRL8OKQzrlsbIbhkM65ZOyikokiPsiaAt4LRbufTcnlx6bk8iUZnlX+/h7U+/4/0vtjH/o29xOaxMGtGdK87vzeSR3fG4jt/eymoxcV7vLM7rncWs6waxakcl7687yAcbDvLeugM4bWYmDsjhJ8M7cH6fbJE3LGgRJyUUFyxYwIIFC5odmzBhAjNmzGDEiBGsW7eOu+66i5deeqnZObEo16OVa216/EjHYpzNYTuC9ovHZeeikd25aGR3AEqqGvh0nV704qM1O/n30o2YTBIj+hZw0YjuTBrZ/aSS29sCuakuLk91cfnQAgD8IZkNe2tYW1TNV7uqeHftAV77bA8AZS9eddz7CZsiONvJSvVwwyWDueGSwYQjMp9t2MOilTtYtHI7H3y5HYDhfQu4/LzeXDa2F51zU49zx/aLJEl0yUqgS1YC147uBOj50rGFp7VFVbzyyS5eWLoTSYJeeUmM6JbOiO4Z/HjI8UM3hT0RtAdsVgsTh3dj4vBuPCcrfP7NXt5evoX3Pt/K28u/w+Wwcsnonky5sB8XDivEZj3+lN1iNjGmVxZjemXxuCEa31t3gP+uP8C7aw+Q4rbxoyH5XDmsgGGF6Wck7FvQvjkpoXj11Vdz9dVXH/X1IUOGUFNTQ0pKCnV1dfHj5eXlZGZmkpWVxZ49e5odz8jIICsri8rKShISEpodq6qqOuxcgaCtk5ueyHUXDeS6iwaiKCrrth1kyaodLF61gwf/bxkP/t8ycjMSuWhENyaN6M4FQ7qc0OphW8TtsHBuz0zO7ZkJgKpqbC9tYG1R9QldL2yK4IeE3WbhwuHduHB4N/50x8V8t7uC/36xlXdXbOW+55dw3/NLGNAtm8vG9uay83rTq1PGWS8+Uj12Jg7IZeKAXEDPwfpmTw2rdlSxemcl/1m5l7mf7uLHQ46/8CTsiaC9YbGYGTekK+OGdOXp313Cym/3seDjzby1/DsWfLyJlAQnV5zfmykX9ufcAR1PKJ3FbJLiv8uPXDuQ5d+V8daaYhas2sc/VuwmP83FFcMKuHJ4B3rlJZ2BTyloj5wy//Pzzz/PkiVLANixYwepqanYbDa6dOnCunXrAFi6dCljxoxhxIgRLF++nEgkQnl5ORUVFRQWFjJ69GgWL17c7Nz8/Hx8Ph8HDhxAlmU+/fRTRo8efaqGLRCcEcxmE8P7FPDgL8az8uVb2P32Xfz93ssZ1jufBR9vZsr//Ju8S2dxye/m8dwbK9lTUtPaQ/5emEwSvfKSuOG8Lid9D2FTBD8EJEmib9cs7r3xfFbNvYWt83/HrNsm4bRb+d+XP+GcG/7CgOue5YEXPmLd1oNH9GCdjTisZkZ0z+B3l/Zi/u/Gsv3Pl7HovnEnfT9hTwTtBbPZxJhBnXn2//2IPe/cxduzpzNpRDfmL9vEpN++Qrer/sQ9f1nM19tLTtge2CwmJg7I5YVfDWfzUz/iLz8fSrecBP66ZAfj/vARF/zxI55fsj1esVggiHHKqp4eOHCA++67D03TkGWZ+++/n/79+1NUVMSDDz6IqqoMGDCA++67D4DXXnuN999/H0mSuOOOOxg5ciR+v5+77rqLuro6EhMTefLJJ0lISGDt2rXxvh8TJ07k5z//+WHvLSqKCdorkajMqk37WbxqJ4tX7WDbvkoA+nbJ4kdjevKjMb0Y2D3nrPcoHIqwKYIfOqVVXt7/fCvvfraFFRv2oigq+ZlJXDmuD1eP78c5PXN/cHbhZBH2RNDe8QcjLFq5nTeWbWLJ6p1EZYVuBWlMv2ggUycNpCCr5V7ByoYQ7607wFtr9rN+dw1mk8QFfbOZOroTE/rniHxGwakTiq2JMMKCs4k9JTW8//k23v98Kys37UdVNfIzk7j03J78aExPxgzs1OL+S4KWIWyKoK1R0xBg0ZfbeXv5d3z01S6iskLn3BSuuqAvV4/vR992mu/8Q0DYE8GppqYhwDvLt/DvpRv5YuM+JEnigiFdmD55ED8e0/OEKqceSlGZl/kr9/LGyn2U14dI9di4akQHrh3did75yaf+QwhOiNmzZ7N+/XpkWebmm2+mX79+3H333SiKQkZGBk8++SQ2W+O/9+9///t4z8Sj9Y9tCUIoCgRtmMpaP4tX7eC9z7c267900cjuXDqmJxOHdyOhneY1tmWETRG0ZWq9Qd77bCsLPt7E8q/3oCgqPTtmcPX4vlw1vh/dO6S39hAFTRD2RHA62VNSwz8Xf8Pri79hX2kdCS47V13Ql+mTBzKyX4cWLyDJisryLeX858u9LPmmhKii0b9jCteO7sgVwzqckirEghNj9erVvPzyy7z44ovU1tZyxRVXMHLkSMaOHcvkyZOZPXs2+fn5TJs2DYAvv/ySp59+msLCQmbNmnXU/rEtQQhFgaCdEAhF+HjtLt7/fBuLVm6nuj6AzWpmwtCuXHF+Hy49tyfJCc7WHuZZgbApgvZCRa2Pd5ZvYcHHm/jy2/1omsaAbtlcNb4fV1/Ql445Ka09xB88wp4IzgSqqvLFxn28tmgDb6/Ygj8YoWt+KtMvGsi0iwbSISu5xfes8YV5a00x//lyL5uL67BbTFx6Tj7Xj+3M8G7pIorhNKMoCuFwGJfLhaqqjBw5ErfbzeLFi7HZbKxfv5558+bx3HPPEYlE+NnPfsZPf/pTPvroI2bNmsXrr7+OLMtCKAojLPihIcsKqzcX8+5nev5ScXk9VouZ8UO6cOW4vlw6picpQjSeNMKmCNojBysbeOvTzSz4eDNrtxwA4NwBHbnuooFccX4fkjyOVh7hDxNhTwRnGl8gzDsrtvDahxv4bMNeJEli0vBCfvbjIUwe2R3LSaSvbN5fx7++2MPC1ftpCEbpnpPIDed15qoRHUn+AXgZ3/+mlHc2lJzSe14+KJcfDcw5oXPnz5/PunXr+OKLL1i1ahUA+/fv5+677+Y///kPzz33HF26dCE9PZ23336bWbNm8X//93+sWLECu90e7x/bs2fPFo3xpNpjCASC1sViMXPuwE6cO7ATs2dcxNotB3hr+Xe8vXwLix9/G+uTZi4Y0oUrz+/DpWN6kproau0hCwSC00xeRiIzrhnFjGtGsaekhvkffcu/lmzklife5XdPf8Cl5/Zk2qQBTBhWKPKcBYKzGI/LzvTJg5g+eRB7S2r5x6KvmffB11xz/7/JSU/gpksGc+Ol59AxO/mE79m3QzKPTRvE//ykH++uLea1z3Yz8z8befStzfx4SD43jO3C4C6pwst4Gli2bBkLFy5k7ty5TJo0KX485uvbu3cvmzdvZsaMGaxZsyb++ogRI+jfv3+z/rHvv/9+i95beBQFgrMITdNYv62Etz7V+y/tK63DYjYx7pwuXDmuDz8a04u0JCEaj4ewKYKzBU3TWLvlAP9aupGFH2+muj5AZoqbqyf047pJA3+QFZXPNMKeCNoCsqzw4aodzH1vHUvWFAEwcXghP/8eXsZN+2t5bcUe3lyzH39Ypk9+Etef14WfDO9AgtN6qj/CD5LPP/+cZ555hpdeeonk5GTGjx/PBx98gMPh4KuvvuL1119n8ODBvPnmmzidTnw+HzU1Nfz85z/nl7/8ZbN7jR49ms8++wyz+cT/rYVQFAjOUjRN4+vtJby9/Dve+vQ79pTUYjGbuHBYIddc2J9LR/fAIwrhHBFhUwRnI5GozJLVO/nXko0sWrmdSFShV6cMpl00kGmTBpCbntjaQzwrEfZE0NbYV1bHq/9dz7wPvqa0yktOegI3XjKYm1roZYzhC0V5a00x/1ixm83FdXgcFqaM6sTPxnWla3bCqf8APxC8Xi/Tpk1j3rx5pKWlAfDAAw8wZMgQLrvsMh555BF69OjB1VdfHb9mzZo18dDT559/nsLCQiZNmsSOHTu48847hUdRGGGB4HA0TeObHaUs/GQzbyzbxIGKelwOK5ee25MpE/ozYVhXbFYRiR5D2BTB2U6tN8ibn2zmX0s2smrTfkwmiYtGdOPGS85h8qjuIjT1FCLsiaCtcqiXUZLgktE9ueXKYZx/TpcWRxtomsaGPTXM/XQX764tJqpoXNA3m1+ML+T83lmYTCJ6oSXMnz+f5557js6dO8ePzZo1i5kzZxIOh8nNzeXxxx/Ham303jYVikfrH9sShFAUCH5gqKrKyk37eeOjTbz56WZqGoKkJjq5clwfpkzoz6j+HTCZfthNdoVNEfyQ2HWgmlc/+JrXPvyGsmovWakepk0awI2XDKZHx4zWHl67R9gTQXtgX1kdL7+7llfeX09VfYBenTL49ZXDmTZpwElFH1XUh/jHit3847PdVNSH6Jrl4afjCpkyqqMIS21HCKEoEPyAiURlPl67i/nLNvH+51sJhKLkZSZyzfh+TLmwP/0Ls3+Q+UvCpgh+iMiywtI1Rcz74Gs+XLkdWVEZ2a8DN10ymCvH9RGh6ieJsCeC9kQoHGXhJ5v525tr+Hp7CYluO9dfPIhfXzGcwoK0Ft8vIqv8d/0BXvq4iK/31OBxWLh2tB6W2iVLhKW2dYRQFAgEAPiDEf775Tbe+Ohblq4pQlZUenfO5LqLBjJ14gBy0n84Bl3YFMEPnfIaH/9c/A2vfvA1O/ZX4XHauHp8P266dDBDe+f/IBeQThZhTwTtEU3T+GrLAf725hre+vQ7orLCxOGF3PKTEUwcXnhSkUdf76lh7idFvLu2GFnVmDQgl1smdmdYYZqwKW0UIRQFAsFhVNcHeOvT7/jnkm9Ys7kYk0niwmGFXHfRQH50bk8c9rM7bETYFIFAR9M0Vm7az6v//Zo3P91MIBRlYLccfnH5UKZM6Ce8jCeAsCeC9k5ZtZe5763jxXfXUVbtpWt+KjOuGcX1kwficrS8h2JFfYhXlu/i1eW7qPFFGNw5lVsmdefiQXmYRR5jm0IIRYFAcEx27K/in4u/4Z9LvuFgRQPJHgdXje/LdRcNZHifgrNyFVDYFIHgcBr8If6z9FtefGctm3eXk+i2M3XSAH552VD6dMlq7eG1WYQ9EZwtRGWFd1Zs4bk3VrF2ywFSE5388vKh/PrK4WSntTzqKBCWmb9yH3//aAd7K/10zHDzqwnduHZ0J9x2UWCvLSCEokAgOCEURWXFhj28/uE3vLNiC8FwlG4FaUy/aCBTJw2kICuptYd4yhA2RSA4OpqmsXpzMS++s5Y3P91MJKowqn8Hfnn5MK44rzd2m5jgNUXYE8HZhqZprNq0n2fmr+T9z7dhtZi49sL+/HbKqJNaNFJUjcXflPC3pdtZt6uGZJeVm87vys8uKCQzyXEaPoHgRDlpofjVV19x++2389hjjzFu3DgAtm3bxh/+8AcAevTowR//+EcAXnrpJRYvXowkSfzmN7/hvPPOw+v1cuedd+L1enG5XDz11FMkJyezcuVK/vSnP2E2mxk7diy33XYbAI899hgbN25EkqTDyrsKIywQnFka/CHeXr6F1z7cwJcb9yFJEuPO6cwNlwzmsjG9Whya2pbsCQibIhCcKFV1fl77cAMvvbuO3QdrSE9yccMlg/nFZUPonJvaKmMS9kQgOHMUFVfzlwWr+MeiDQTDUS4cVsjt147igiFdTyriaG1RFS98tJNFGw5iNZuYMqojt03qQadMz2kYveC4aCfBvn37tF//+tfabbfdpn3yySfx49OnT9c2btyoaZqm/fa3v9WWL1+u7d+/X7viiiu0cDisVVdXaxdeeKEmy7L23HPPaS+++KKmaZr2+uuva7Nnz9Y0TdMmT56slZSUaIqiaFOmTNF27typrVmzRvvVr36laZqm7dy5U7vqqquajae4uFjr3r27VlxcfDIfRyAQfA92H6zWHn75Y637VU9pjnMf0HImP6b97un/aht3lp7Q9W3NnmiasCkCQUtRFEX7aM1O7er7/qW5xj6oOcc8qF1592vax2uLNFVVz9g4hD0RCFqHqjq/NuvV5VrHHz+hOc59QBt641+01z/coEWi8kndb1dZg3bXa+u1Dr9+U8v55QLt1hfXaFsO1J3iUbd9nnjiCe2aa67RrrzySm3JkiVaSUmJNn36dG3q1Knab3/7Wy0cDjc7/3e/+512zz33aJqmaZFIRPv973+vXXvttdp1112n7d+/v8Xvf1LN0jIyMvjLX/6Cx9Oo7iORCAcPHoyvpI0fP55Vq1axZs0axowZg81mIzU1lby8PIqKili1ahUXXnghABMmTGDVqlUUFxeTlJRETk4OJpOJ8847j1WrVrFq1SomTJgAQGFhIQ0NDfh8vu+rkQUCwSmgc24qM392AVvn38EHT9/I+KFdefm9dQz/6V8Z/csXjnu9sCcCQfvHZDIxYVghbzw2le0Lfs+9N4xl3daDXPK7Vznnhr/w4jtr8Qcjp30cwp4IBK1DWpKLe244j+0Lfs/f770cVdP4xaNv0XfqM7zw1hqC4WiL7tclK4HZ0wfz1eOTufnC7nz4zUHG/eEjfvr8SjbsqTlNn6JtsXr1anbu3Mn8+fN56aWXeOyxx3j22WeZNm0a//rXv8jLy2PhwoXx87/88kv2798ff/7f//6XxMRE/v3vf/PLX/6Sp556qsVjOCmh6HQ6MZvNzY7V1taSmJgYf56RkUFlZSVVVVWkpjaGn6Snpx92PD09nYqKCiorK496bkpKSvx4WloalZWVJzN0gUBwmjCZTFwwpCuv/fEadr99F3NuvxhZVo97nbAnAsHZRX5mEg/+Yjw7Ft7JS/9zJQ6bhd8+9T6FV87hvueXsK+09rS9t7AnAkHrYrdZuOGSwayddxtvzrqOnPQEfvf0B/S8+k88+fpn1PtCLbpfVrKTh67uz7pZF3Pnj3qxakclkx/7hClPf8bK7ZVo7b/UylEZOnQozzzzDABJSUkEg0HWrFnD+PHjgcZFL9AXxP72t79xyy23xK9vuuh17rnnsn79+haP4bgZ5wsWLGDBggXNjs2YMYMxY8Yc87rYP9yh/4CapiFJUrPjRzoW40jHY+cLBIK2SVqSi9uuGsFtV41odlzYE4Hgh4PdZuG6iwYybdIAVm3az1/fXMNzC1bx7BsrufTcntx21QjGDOx00t8/YU8EgraLJElcPLoHk0d154tv9vLk65/z4N+X8dQ/v+DmK4Zx29UjyEw58bzDVI+du37ch1smdufVFbt5YekOrpyzgqFd0/jtxT2Z0C/7tH733lp7gAVfHTil97x6WD5XDj163rLZbMblcgG6vRs7dixffPEFNpvekiS26AXw97//nalTpzaLpmi66GU2mzGZTEQikfj1J8JxheLVV1/N1VdffdwbpaamUldXF39eXl5OZmYmWVlZ7Nmzp9nxjIwMsrKyqKysJCEhodmxqqqqw861WCzNjldUVJCenn6in1EgELQRhD0RCH54SJLEqP4dGdW/I8Xl9bz4zlfMfX897322lX5ds/jN1SOZcmH/FldLFfZEIGj7SJLEmEGdGTOoM19vL2HO65/z5Ouf89wbq7jp0sHcMXU0HbKST/h+HoeV2yb14GfjCvn3l3t4fvEOrn/uS/p3TOH//agXF/bPOesWa5YtW8bChQuZO3cukyZNih+PLVTt3buXzZs3M2PGDNasWXPY602ft/Rvc8pqWFutVrp06cK6desYMmQIS5cu5frrr6dTp0688sorzJgxg9raWioqKigsLGT06NEsXryYW2+9laVLlzJmzBjy8/Px+XwcOHCA7OxsPv30U+bMmUNtbS3PPfcc1157LVu2bCEzM7OZYhYIBGcXwp4IBGcnBVlJ/O/NF3LfTecz/6NveX7Bam6e9Q4P/d8ybr1qBL+4fCgpCc5T+p7CnggEbYPBPXL518NT2L6vkj/96wtefGctL76zlusuGsg9N4xtUaVkp83Mz8YVcv2YLixcs5+n/7uVG/6ykoGdUrjrx725oO+p9TBeOfTY3r/Txeeff84LL7zASy+9REJCAk6nk1AohMPhiC96LV++nJKSEq655hp8Ph81NTW8+OKL8UWvnj17Eo1G0TQNq7VlVelPqj3G8uXLefnll9m9ezepqalkZGQwd+5cioqKePDBB1FVlQEDBnDfffcB8Nprr/H+++8jSRJ33HEHI0eOxO/3c9ddd1FXV0diYiJPPvkkCQkJrF27ljlz5gAwceJEfv7znwMwZ84c1q1bhyRJPPTQQ/Ts2TM+HlF6WiBov7Q1ewLCpggEZwpN0/hk3S7+/O8vWbZ2F26njZsuHcyMq0fSMSfl+Dc4BGFPBIL2w/7yOv787y955f31RBWV6yYN4J4bzqNLXstb60RllQWr9vH0B1sprg4wuHMqd/24N+f3yWq3Hkav18u0adOYN28eaWlpADzwwAMMGTKEyy67jEceeYQePXo0i6xYs2YNb7/9NrNmzeL9999n9erVPProoyxdupSlS5fGbdiJctJ9FNsSwggLBIJTibApAsGZZ1NRGc/MX8n8j75F1TSuPL8Pd0wdzTk981p7aN8LYU8EgmNTWuXlT//6gpfeXfu9BWNEVnlj5V6e/mAbB2sCDO2axl0/7s2YXpntTjDOnz+f5557js6dO8ePzZo1i5kzZxIOh8nNzeXxxx9v5iVsKhQVRWHmzJns3bsXm83GrFmzyMnJadEYhFAUCASCQxA2RSBoPQ5U1PPXhat5+b11NPjDjBnYid9NHc2kEd0wmU6qWHurIuyJQHBinGrB+O8v9vDMom2U1AYZXpjGXT/uw+ieGe1OMLYm7c/iCgQCgUAgOGvJz0zisVsnsfPNO5l12yT2lNRy5T3/5Jwbnm/toQkEgtNITnoCT/52Mlvm/45brhzOG8s20f+6Z7n58bfZfbBlvRNtFhM3nt+VVY9exOPTBrG/KsBVf/qMq576jHW7qk/TJzj7EEJRIBAIBAJBmyPR7eD2a0ezZf4dvPLgVdht5uNfJBAI2j1NBeOtP2kUjL+e9Q77yupadC+71cxPx3Vl1WMX8ci1A9he0sClsz7lhr98eXoGf5YhhKJAIBAIBII2i9Vi5toL+7Pq5VuOf7JAIDhryElPYPaMyWx9Q/cw/uejb+k/7RnufGYR5TW+Ft3LYTXzi/HdWPP4ZO6/oi9rdlYd/yKBEIoCgUAgEAjaPiKvSCD4YZKdpnsYN/3rdqZfNJC/v/0Vfa79M394cRl13mCL7uW2W/jtxT1Z89jk0zTaswshFAUCgUAgEAgEAkGbpiAriefvvoxvXpvBJaN78MQ/PqPXNU/z5Ouf4Q9GWnSvZLftNI3y7EIIRYFAIBAIBAKBQNAuKCxI49WHrmbNK7cyqn8HHvz7Mvpc+2f+9uYawhG5tYd3VmFp7QEIBAKBQCAQCAQCQUvoX5jNm09MZ9Wm/Tz0f8v4/Z8/4M//+ZIHf34BUyf2b5ftdA5l9uzZrF+/HlmWufnmm+nXrx933303iqKQkZHBk08+ic1m4/nnn+ezzz5D0zTOP/98br31VpYsWcKcOXPIzs4GYNSoUdxyS8tyvYVQFAgEAoFAIBAIBO2Skf06sOTZn/Lx2l08+PeP+MWjb/HcGyt57NZJXDCka2sP76RZvXo1O3fuZP78+dTW1nLFFVcwcuRIpk2bxuTJk5k9ezYLFy5k7NixbN++nfnz56MoCpMnT+YnP/kJgUCA6667jptuuumkx9D+pbZAIBAIBAKBQCD4wSJJEhOGFfLFizcz76GrqPOFuOR3r/LjO//BpqKy1h7eSTF06FCeeeYZAJKSkggGg6xZs4bx48cDMH78eFatWkV+fj7PPvssAPX19UiShMfjwe/3f+8xCI+iQCAQCAQCgUAgaPeYTCamTOjP5WN787e31vDEqysY/rO/cf3kgTzw8wvIz0w6qfu+sXIf//5yzykd69TRnblmVMejvm42m3G5XAAsWLCAsWPH8sUXX2Cz6YV4MjIyqKysjJ//yCOPsGjRIu655x7cbjeBQIAVK1bEQ1Lvueceevbs2aIxCo+iQCAQCAQCgUAgOGuw2yzcce1ovpt/B7+dMtLowfgsD/3fMhr8odYeXotYtmwZCxcu5MEHH2zWJkjTtGbnzZw5kw8//JCXX36Z4uJiRowYwYwZM5g7dy633XYbd911V4vf+6Q9il999RW33347jz32GOPGjQPg17/+NfX19Vgs+m3vuece+vbty0svvcTixYuRJInf/OY3nHfeeXi9Xu688068Xi8ul4unnnqK5ORkVq5cyZ/+9CfMZjNjx47ltttuA+Cxxx5j48aNSJLE/fffT//+/U926AKBoI0h7IlAIDhVCHsiEAhipCa6mHXbRfz6iuH84aWPmf3aZ8x9bx33/3Qct/xk+Anf55pRHY/p/TtdfP7557zwwgu89NJLJCQk4HQ6CYVCOBwOysvLyczMpLS0lKqqKvr160dSUhKDBw9m06ZNXHzxxfH7DBkyhJqaGhRFwWw2n/gAtJNg37592q9//Wvttttu0z755JP48enTp2v19fXNzt2/f792xRVXaOFwWKuurtYuvPBCTZZl7bnnntNefPFFTdM07fXXX9dmz56taZqmTZ48WSspKdEURdGmTJmi7dy5U1uzZo32q1/9StM0Tdu5c6d21VVXNXuP4uJirXv37lpxcfHJfByBQNCKtDV7omnCpggE7RVhTwQCwbFYt/WANnHGy5rj3AdaeyjHpaGhQbv00ku1qqqq+LGZM2dq77zzjqZpmvbwww9rb7zxhrZ582btyiuv1KLRqCbLsnbVVVdpW7Zs0f7yl79oixcv1jRN07Zv365deumlLR7DSYWeZmRk8Je//AWPx9Ps+JGSJtesWcOYMWOw2WykpqaSl5dHUVERq1at4sILLwRgwoQJrFq1iuLiYpKSksjJycFkMnHeeeexatUqVq1axYQJEwAoLCykoaEBn893MkMXCARtDGFPBALBqULYE4FAcCzO6ZnH4md+ypuzrmvtoRyXRYsWUVtbyx133MH111/P9ddfz69//Wveeecdpk2bRl1dHZdffjl9+vRh4sSJTJ06lSlTpnDeeefRq1cvLrvsMl5//XWmT5/Ogw8+yKOPPtriMZxU6KnT6Tzi8UAgwB//+EdKS0vp3r079913H1VVVaSmpsbPSU9Pp7Kystnx9PR0KioqqKysPOzc4uJiamtr6dOnT/x4WloalZWV8R8CRVEAKCtrn1WNBIKznezs7HjI16G0NXsCwqYIBG0ZYU8EAsH3pX9HNwcOHDimPWltpkyZwpQpUw47/sorrxx27Oabb+bmm29udiw/P5/XXnvte43huH+ZBQsWsGDBgmbHZsyYwZgxY444yNGjR5ORkcGDDz7IP//5z8MSLTVNQ5KkZsePdCzGkY7Hzo8Rq/hz3XVtf3VAIPgh8vHHH5Ofn98u7AkImyIQtGWEPREIBKeKmD0RHJnjCsWrr76aq6+++oRudsUVV8T3J0yYwKJFixg+fDh79jSWky0vLycjI4OsrCwqKytJSEhodqyqquqwcy0WS7PjFRUVpKenx5/37duXf/7zn2RkZLQsQVMgEJwRsrOzgfZhT0DYFIGgLSPsiUAgOFXE7IngyJwyX6uiKPzsZz/j+eefx+PxsGbNGrp168aIESN45ZVXmDFjBrW1tVRUVFBYWMjo0aNZvHgxt956K0uXLmXMmDHk5+fj8/niruBPP/2UOXPmUFtby3PPPce1117Lli1byMzMbBbW4XA4GDJkyKn6KAKBoJVpTXsCwqYIBGcTwp4IBALBySFpR4qnOA7Lly/n5ZdfZvfu3aSmppKRkcHcuXN59913efXVV3E6nWRlZfHoo4/idDp57bXXeP/995EkiTvuuIORI0fi9/u56667qKurIzExkSeffJKEhATWrl3LnDlzAJg4cSI///nPAZgzZw7r1q1DkiQeeuihFjeMFAgEbRNhTwQCwalC2BOBQCA4dZyUUGxrtNceRrNnz2b9+vXIsszNN9/MxIkTW3tIJ0woFOKSSy7htttu48orr2zt4ZwQ7733Hi+99BIWi4Xbb7+d8847r7WHdFz8fj/33HMP9fX1RKNRbrvttiPm37QlduzYwa233spNN93E9OnTKS0t5e6770ZRFDIyMnjyySex2WytPcyjIuzJmac92hNofzZF2JPWQdiUM097tCntzZ5A+7MpZ4M9OdOcVHuMtsRXX33Fvn37mD9/Po888ggPP/xwaw/phFi9ejU7d+5k/vz5vPTSSzz22GOtPaQW8be//Y3k5OTWHsYJU1tby/PPP8+//vUvXnjhBZYtW9baQzoh3n77bTp37sxrr73GM888c1Kljc8kgUCAhx9+mJEjR8aPPfvss0ybNo1//etf5OXlsXDhwlYc4bER9qR1aG/2BNqnTRH25MwjbErr0N5sSnu0J9C+bMrZYE9ag3YvFNtrD6OhQ4fyzDPPAJCUlEQwGIyX0G7r7Nq1i6KiIs4///zWHsoJs2rVKkaOHInH4yEzM7Pd/FinpKRQV1cHQENDAykpKa07oONgs9l48cUXyczMjB9bs2YN48ePB2D8+PGsWrWqtYZ3XIQ9OfO0R3sC7dOmCHty5hE25czTHm1Ke7Qn0L5sSnu1J7Nnz2bKlCn85Cc/YenSpZSWlnL99dczbdo0br/9diKRCADPP/88U6ZM4ZprruGvf/0rANFolDvvvJOpU6cyffp0iouLW/z+7V4oVlVVNfuPGeth1NYxm824XC5Ab0EyduzYdlMN7YknnuDee+9t7WG0iAMHDqBpGnfccQfTpk1rk8bgSFxyySWUlJRw4YUXMn36dO65557WHtIxsVgsOByOZseCwWA8lCMjI6NNfz+FPTnztEd7Au3Tpgh7cuYRNuXM0x5tSnu0J9C+bEp7tCdH8uwfyQt64MABtm/fzvz58/n3v//NO++8Q3l5Of/9739JTEzk3//+N7/85S956qmnWjyGdi8UT6SHUVtm2bJlLFy4kAcffLC1h3JCvPPOOwwcOJCCgoLWHkqLKS8vZ86cOcyaNYv77rvviH2x2hrvvvsuubm5fPTRR7z66qvtZpWxKU2/j239by7syZmlPdsTaH82RdiTM4+wKWeW9mxT2ps9gfZvU9q6PTmSZ/9IXtD8/HyeffZZAOrr65EkCY/Hw6pVq7jwwgsBOPfcc1m/fn2Lx3DK2mO0Fof2NjpSD6O2yueff84LL7zASy+9REJCQmsP54RYvnw5xcXFLF++nLKyMmw2G9nZ2YwaNaq1h3ZM0tLSGDRoEBaLhQ4dOuB2u6mpqSEtLa21h3ZMvv76a84991wAevbsSXl5ObIsY7G0n6+u0+kkFArhcDgoLy9vFvbR1hD25MzSXu0JtE+bIuzJmUfYlDNLe7Up7dGeQPu3KS2xJ//6bBevf1p0St9/+rhCpo3tetTXj+TZ/+KLL47qBX3kkUdYtGgR99xzD263m6qqKlJTU+P3MplMRCKRFhXsafcexdGjR7NkyRKAo/Ywaot4vV5mz57N3//+93aVcP3nP/+ZN998kzfeeIOrr76aW2+9tc0bYNBXUlavXo2qqtTU1BAIBNp0LH2Mjh07snHjRgAOHjyI2+1uNwY4xqhRo+Lf0VhPsraKsCdnlvZqT6B92hRhT848wqacWdqrTWmP9gTav01pL/akqWf/WF7QmTNn8uGHH/Lyyy9TXFx8SiIa2s+/5lEYPHgwffr04dprr433MGoPLFq0iNraWu644474sSeeeILc3NzWG9RZTFZWFpMmTeLGG28kGAwyc+ZMTKa2v04yZcoU7r//fqZPn44sy/zhD39o7SEdk82bN/PEE09w8OBBLBYLS5YsYc6cOdx7773Mnz+f3NxcLr/88tYe5lER9kRworRHmyLsyZlH2BTBidAe7Qm0L5vyfe3JtLFdj+n9O10c6tk/khe0tLSUqqoq+vXrR1JSEoMHD2bTpk1kZWVRWVlJz549iUajaJqG1Wpt0fufFX0UBQKBQCAQCAQCgeBswev1Mm3aNObNmxcPQ37ggQcYMmQIl112GY888gg9evSgd+/ePPjgg8yfPx9Jkrj22mv53//9X4qKili9ejWPPvooS5cuZenSpcyZM6dFY2j3HkWBQCAQCAQCgUAgOJs4kmd/1qxZzJw5s5kX1Gq1MnHiRKZOnYqmaZx//vn06tWL7t27s3LlSqZOnYrNZmPWrFktHoPwKAoEAoFAIBAIBAKBoBltPwBaIBAIBAKBQCAQCARnFCEUBQKBQCAQCAQCgUDQDCEUBQKBQCAQCAQCgUDQDCEUBQKBQCAQCAQCgUDQDCEUBQKBQCAQCAQCgUDQDNEeQ3DSVFVVsXHjRsaPH3/G3vOCCy5g9uzZDBky5ISv+Z//+R+ys7OZMWPGMc/78ssvee6556irq0NRFPLy8pg5cyaFhYUA/Pe//+Xll18mGAwiyzLdu3fnoYceIisri7feeov//d//JTs7GwBVVenTpw8PPPAAqampRKNRHn30UVavXo2maQwfPpwHHnigxY1PBYKziXvvvZcOHTpw6623ttoYnnvuOcrKynj00UdP+Jp169Zx991388knnxzzvJqaGh577DE2bdoEgMVi4cYbb+Saa64BoLi4mMcee4zdu3cD4HQ6+c1vfsOECRMA6NGjBx06dMBsNqNpGh6Ph//3//4fI0eOBGDJkiX89a9/JRwOk5KSwh//+Ee6d+/e4r+BQHC2cqZtzMm837vvvsvChQt57bXXjnne8ezF5s2befLJJykvL0fTNJKTk7nrrrsYMmQIBw4cYPz48XTu3BkATdNIT0/nf/7nf+jduzcA//rXv/jnP/+JLMvk5+fzyCOPkJOTczJ/BsFZhBCKgpNmzZo1rFy58owKxdNFQ0MDt99+O6+++ip9+vQBYN68ecyYMYNFixaxa9cuHnvsMd544w3y8/NRFIXZs2dz//338/LLLwMwcOBA5s2bB+hC8eGHH+bhhx/m6aefZu7cudTU1PDBBx8gyzI33HADb7zxBtddd11rfWSBQHCaefjhh8nNzWX27NmYTCb27t3LlClT6NatG4MGDeLOO+/ksssu429/+xsAGzdu5MYbb+TDDz+MT9Bee+21+ALU+vXrueWWW1i8eDGhUIiHHnqIN998k7y8PF599VXuv/9+Fi5c2GqfVyAQnD6OZS+ys7P59a9/zSOPPML5558PwNKlS7nttttYvnw5AGazmcWLF8fvt2jRIm677TaWLFnC5s2bmTt3Lm+++SZJSUk8/vjjzJo1i2eeeeZMf0zBIcyePZv169cjyzI333wz/fr14+6770ZRFDIyMnjyySex2WwsWrSIuXPnYjKZGDlyJL/73e9YsmQJc+bMif+GjBo1iltuuaVF7y+EYjvi448/5s9//jOBQICOHTsyZ84cUlNTuffee0lKSmLLli0cPHiQvn378sQTT+B0OtmwYQMPP/wwgUAAk8nEzJkzGTVq1DHf57nnnqOqqoqysjK+++47Ro4cyeTJk/nLX/5CRUUFDz/8MJmZmfzv//4viqIQCAR4+umnj3q/HTt28MADD+Dz+YhGo9xwww1Mnz6dUCjEgw8+yLp167Db7fz617/msssuIxgMct9997F161ai0SiTJk3innvuOeG/R21tLXfeeSd79+6lsLAQh8MR/5Icjb179yJJEj179owfu/7667n44ouRJImdO3eSlpZGfn4+oBvc3/3udwSDwSPez2Qycd111zFt2jQAhg4dykUXXYTZbMZsNjN48GD27NlzzDEJBKebM2FTioqKmDZtGitXrsRi0X9ybrnlFsaOHRs/54knniAcDvPggw8CUF9fz9ixY/n0009JTU097J6yLPOHP/yBtWvXoqoqPXr0YNasWXg8Ht5++21eeOEFAPr378+jjz6KzWZjwYIFzJ07N/7jOnv2bPLy8prdt7y8nIceeij+3bz//vs577zzAPjrX//K/PnzSU1NZdy4cSf0992xYwcXXXQRJpOe5dGpUyfef/990tLS4q8PGDAgfv6AAQNYsmQJmZmZR7zfOeecQ4cOHdiwYQP9+vXjqaeein+GkSNH8uyzz57QuASCU01r25JzzjnniPOME8Hv93P33Xeze/duIpEII0eO5KGHHsJqtfJ///d/zJ8/H4vFwvnnn8+9996LJEk8//zzvPfeeyiKQteuXXnyySdJTEw8bLx/+MMfqKysxGaz8dhjj9GvXz9UVeWRRx7hk08+IT09naFDh57QOI9lL2pra6msrGz2+sSJE+nfvz9Op/OI97v44ot5+OGH2b17N2lpacyePZukpCQARowYccx5neDMsHr1anbu3Mn8+fOpra3liiuuYOTIkUybNo3Jkycze/ZsFi5cyBVXXMGcOXN47733cLvdXHPNNfzoRz8iEAhw3XXXcdNNN538IDRBu6CkpEQbOnSotn37dk3TNO2FF17QZsyYoWmapt1zzz3auHHjtJqaGk1RFO26667T5s2bp2mapl166aXaf//7X03TNO3tt9/WJkyYcNz3evbZZ7UxY8Zo1dXVWk1Njda3b1/toYce0jRN01577TVt6tSp8fPuv//+495vxowZ2ltvvaVpmqZVV1drt9xyixYOh7Xnn39eu+OOOzRN07TS0lJtyJAhWllZmfbyyy9rv/jFLzRVVbW6ujpt2LBh2tq1azVN07Rx48Zpa9euPebf44knntB+//vfa5qmacXFxdqgQYO0Z5999phjDAQC2vnnn69NnTpVe//997Xy8vJmr5eVlWnnnHOOdvPNN2sfffSRVltb2+z1N998U7vxxhubHdu6das2evTow96rvLxcu/jii7UvvvjiuH87geB0cSZtyuTJk7VVq1ZpmqZ/1wYNGqRVV1dr99xzj/b8889rmzdv1kaOHKlFo9H4fX/2s58d9X6ffvqpdsMNN2iqqmqqqmpPP/209tlnn2nFxcXaiBEjtLKyMk1VVe22227TXnzxRa2qqkrr27evVlpaqmmapt17771x29XUjv3qV7/Snn76aU3TNG3v3r3asGHDtJqaGm3nzp3a0KFDtcrKSk2WZe3WW2/Vxo0bd9zPPWvWLG3EiBHa3//+d23Lli2aoijNXp8xY4Y2btw47R//+IdWVFR02PXdu3ePjznGZZddpn322WfNjkWjUW3WrFnaXXfdddwxCQSnmrZgS442z4jZmGPx+uuva/fee6+mafp36cEHH9S2bNmirV27Vrvwwgs1r9erhcNh7Sc/+Ym2aNEibdOmTdrIkSM1r9erKYqi3XTTTfH3iL2foijapZdeqr3xxhuapmnaunXrtHPPPVeLRqPa8uXLtYkTJ2o+n08LBoPaVVddpU2fPv24n/1Y9kJVVe0nP/lJ/D2Li4ubvV5cXKz16tXrsHsOHTpU27VrV7NjwWBQ+93vfqf9+c9/Pu6YBKcXWZY1v9+vaZqmKYqiDRs2TBs3bpwWDoc1TdP/X/3mN7/RNE3TvF5v/Lpf/OIX2tq1a7XXXntNe+WVV77XGIRHsZ3wySef0K9fv3j+ydSpUxk1ahSKogB67l5KSgoAEyZMYMOGDdx444288847SJIE6KvRxcXFJ/R+gwcPjq/kZ2RkxFfVu3fvHg+vPFHS0tJYsmQJ3bt3p3fv3vz1r38F4LPPPuMXv/gFANnZ2Sxfvhy3283PfvYzrr/+eiRJIikpiW7dunHgwIFmEmiR6gAAmxFJREFUeYnH+nusW7eOX/3qVwDk5+czbNiw447R6XTyn//8h1deeYVnn32Wffv20b9/f+666y6GDRtGVlYWCxYs4JVXXuHhhx+moqKC4cOHc++99zbzQsaIRCK88sorXHjhhc2OX3fddWzatImf/vSnx/XsCgSnkzNpUyZOnMgnn3zCiBEj+Pzzz+nfv38zT2GfPn1ISEhg1apVjBkzhmXLlnHxxRcf9X6pqans2rWLjz76iHPPPZc77rgDgPnz5zNo0CCysrIAeOqppzCbzVgsFtavX4/NZgNgyJAhvPvuu83uGQgEWLFiBQ8//DAAHTt25JxzzmHFihUEg0GGDh1Keno6AD/+8Y/ZunXrcT/3XXfdRefOnXn//fd59tlnSUxM5LrrruOWW27BZDLx5JNP8vrrr/Pee+/x2GOPkZOTwy9/+UumTp16xPutWLGCqqoqBg8eHD/26quv8te//pUOHTr8f/b+PE6uusr/x593qb2q9707WychG0lYAkQgoAREcENZBAZcfujofFBxZFRAvogrDCPOqOPH8SMz6qgzIujMwIgBQYIgMZAEkkAI2Zfe1+ra666/P+5SVZ0OZGlIJ7yfj0c9qvrWrVu3Krmnzuuc8z6HH/zgB697TgLBZDMVbMnB/IxDoa6ujhdeeIFnnnmGM888k69+9asAfOc73+H8888nHo8DThl4MBhEURRWr17t25NTTz31gHPftWsX+/bt4/LLL/c/n/c+zz//POeffz6xWAyASy65hCeffPJ1z/O17IUkSfzkJz/hJz/5Cf/+7//u91e46aabeOc733nAsWzb5te//jXNzc3MnDnT337PPffwq1/9itNPP933zwQOv3ziFX72hy2TesyPXLSQv1q54KDPK4pCNBoF4IEHHuC8887jmWee8f/vNTY2Mjg4COD/P922bRvd3d0sXbqUDRs28NRTT/GnP/0J27b50pe+NKHP+loIoXickE6n2bhxI+9617v8bfF4nGQyCUBNTY2/vaqqilQqBcDDDz/Mv//7v5PNZrEsC9u2D+n9PAMGlf9RZVnGsqzDOve/+7u/40c/+hGf+9znKBaLfPKTn+Sv/uqvGB0dJZFIHPCee/bs4e6772bXrl3IskxfXx8f/OAHK475Wt/H2NhYxXHHl4McjObmZm655RZuueUWurq6+OUvf8knP/lJnnzySWpqapg1axZf+9rXANi5cyf/7//9P/76r//ar/9/8cUX/fPxasT/7u/+ruI9fvnLX5LJZLj11lv59re/zRe+8IVD/BYFgsnlzbQp73rXu/j0pz/NbbfddlAR+J73vIf//d//5YwzzuC55557zeYyS5Ys4fbbb+fnP/85X/rSl7jgggv4yle+wujoaMX1HgqFADBNk+9///s88cQTmKZJNpv1mzqUfx+2bfPhD3/Y35bL5Vi+fDm5XO6IbIosy1x11VVcddVV5HI5Vq9ezde//nXq6+u5+uqrCYVC3HDDDdxwww2kUilWrVrFt771LTo6OlixYgXglMB7zWza29v58Y9/XGGfP/KRj/DhD3+Y3/3ud1x99dU88sgjhMPhQzo/gWAymAq25GB+xqFwySWXMDY2xne/+1127drF+973Pm699VZGR0crysC9Es58Ps9dd93F2rVrAadU3lsX6JFKpTBNs8LWZTIZ30cpP+6h2pPXsxeJRILPfvazfPazn2VoaIjf/va3fP7zn+d//ud/CIVCmKbp/xvZts2cOXP4v//3//ql8QBf/OIX+fznP8+//du/8bGPfYxf//rXh3RugjeWxx9/nAcffJB/+7d/4+KLL/a3j79m9uzZw80338y9995LIBBg+fLlLFmyhOXLl7Nu3Tq+8IUv8PDDDx/WewuheJzQ1NTE2WeffdA1KKOjo/7jsbExqqur6e/v5/bbb+eBBx5gwYIF7Nmzp+I/2JtFLBbj85//PJ///OfZtGkTn/jEJzj77LOpra2tOO++vj6qq6v52te+xqJFi/jBD36AoihcffXVBxzztb6Pqqoq0um0//fIyAjTpk17zXPcvXs3uVzOb2TT0dHBl770JX7729/S1dVFT08P4XCYzs5OAGbPns3/9//9f5x++umMjY0Blc1sxvP444+zcOFC2traiMfjfOADH+C73/2uEIqCY8abaVPmz5+Poihs3bqVZ555hltvvfWAfd797ndz1VVXcd5553Haaaf5a2UOxrve9S7e9a53kUwm/aZSbW1tvPDCC/4+mUyGQqHAmjVreOKJJ/jFL35BXV0dv/71rw/4sayvr0dRFH7zm99UCDFwugGW25Ty7+ZgZLNZnnvuOX89YzQa5dJLL2XTpk1s376dkZERXnnlFc455xzAsVtXXXUVTz/9NNu3b/eFYnkzm3J27txJf38/Z599NpIk8Z73vIevf/3r7N69mwULDh6hFggmm6lgSw7mZxwqV199NVdffTX9/f185jOf4b//+78P8FG8x/fffz979uzht7/9LbFYjH/8x3+kv7//gO8kFotVNI/xePHFFw/wUV6P17MX4yuvGhoa+Ou//mtWrVrFjh07WLRo0QHNbMrZtGkTtm2zdOlSVFXl2muv5d577yWVSh2ykD3R+auVC14z+/dG8fTTT/Mv//Iv3HfffSQSCSKRCIVCgXA4TH9/vx906Ovr48Ybb+See+7xfwOWLFniH2fZsmWMjIxgmiaKohzy+4s5iscJ55xzDuvWrfPLGzZt2sQ3vvEN//mnn37aj2A9/vjj/n+IaDTKrFmzMAyD+++/H3Ccp8lAVdUKY3cwPvWpT7F9+3bAKV2Nx+PIsswFF1zAf//3f2PbNoODg1x22WWMjIwwPDzMggULUBSFP//5z+zdu5dsNnvI38cpp5zC448/DsC+fftYv379657jK6+8wmc/+9mK8pHVq1ejKAqdnZ0888wzfOlLX2JoaAhwojgPP/wwc+bM8UtqXosnnniC73//+37UdPXq1cybN+91XycQvFG82Tblne98J9///vdZsGDBhNdMZ2cn06dP59577+WSSy55zWP95je/8cssa2pq/ADO+eefz4YNG+jq6sK2bb7yla/w4IMPMjw8THt7u+/4PfLIIwfYFFVVOe+88/jVr34F4DfV6u3t5bTTTmP9+vX+j+xDDz30up9XkiRuvfVWfvvb3/rbhoaG+POf/8yyZcvI5/N89rOf5emnn/af37t3Lxs3buT0009/3eOPjIzwxS9+0XdQ169fj67rrxsUEwgmm6lgSw7mZxwKP/jBD/xuwc3NzXR0dCBJEhdccAF//OMfGRsbwzAMbrzxRp555hmGh4eZNWsWsViM7u5uVq9efYA9aW9vp6WlxRdmIyMjfP7znyeXy3HqqafyzDPPUCgUyOfzBxVv5byevejt7eXGG2/kpZde8p/ftGkTPT09nHzyya97/F27dnH77bf7Pt2TTz5Ja2urEInHmHQ6zT333MOPfvQjPzN/9tln8+ijjwJOZ1svqPjlL3+ZO++80094gPN/29t327Zt1NXVHZZIBJFRPG5obm7m61//OjfeeCO6rhOLxbjtttv855cvX86nP/1p9u3bx5IlS7j88ssJhUKcd955XHDBBbS2tnLLLbewYcMGrr32Wh566CE+8pGP8MUvfrHiP9XhcM455/CTn/yEyy+/nN/85jcHPd51113HzTffjK7rAFx77bXMmDGDj370o+zdu5d3vOMdhMNhvvSlL9He3s7f/M3f8I1vfIN//ud/5qKLLuLTn/403/nOd/xZP6/3fXzyk5/kb//2b7nggguYPXt2RX3+vffeS1tb2wFrgC699FLS6TQ33ngjxWIR0zSZMWMG9913H9FolE984hNYlsWHP/xhTNPEMAwWLVrkd1d8Pb70pS/xta99jUsuucQv+fDKWAWCY8GbbVPe9a538cEPfrDCgRzPu9/9br773e/6I3d+8YtfMDQ05K9B9Fi5ciW33XYb73znO1EUhRkzZnD33XdTU1PD1772NT7ykY+gKAqLFy/mYx/7GOl0mt/97ne84x3voLOzk7/927/17Ux55vKrX/0qX/nKV3jggQcAZy1ia2srra2tXH311XzgAx+gpqaGd7/73Wzbtg1wnLHvfve7/pgcj2g0yk9/+lPuvfde304EAgG/Wx3AD3/4Q773ve/xjW98A9u2icVi3HrrrRWdCw/GGWecwac+9Sk+9rGPYVkWwWCQf/zHf/TXqQgEbxZTwZYczM8o52C//+9///u59dZb+fGPf4wkSSxdupT3v//9BINBbrjhBi677DKCwSArVqzgPe95DwsXLuQzn/kMF1xwASeffDK33norN954Iz/5yU/8Y0qSxHe+8x3uvPNO/umf/glZlvnYxz5GNBrlHe94B6tXr+biiy+moaGB888/n3Xr1gHwhz/8gT/+8Y/cddddFefY3t7+uvbi61//OnfeeSfpdBrLsqivr+cf//EfaW9vp6ur6zX/Dd///vezZ88errzySmzbpqqqSozGmAI88sgjjI6OVvwG3n333dx+++3cf//9tLW1cdlll7F7927WrVtXkdX/6Ec/6v/f/vnPf45hGIc1L9hDsg910ZpgynKkA2V/+MMfcsEFF0xaZmuyj/dGsH79erZs2cL1119/rE9FIJiyHCub8sgjj/Doo4/6Dkp/fz8/+clPuOWWW47oeG8Wf/u3fytayQsEEzBV/BM4Pn7/dV3ny1/+Mvfcc8+xPhWBABClp29pOjo6/C5lU/F4bwSaplUsuBcIBJPH0diAfD7PfffdV+HEDQ4OcuWVV07W6b0heLOtBALB5PFG+BPHw+9/b2+vP39ZIJgKHFXp6T333MP69esxDINPfvKTrF27lhdeeMFvBHDDDTfw9re/nYceeoif/exnyLLMhz70Ia644gp0XeeWW26hp6cHRVG46667mDZtGlu3buXOO+8EYN68eX6b4vvuu49Vq1YhSRKf/vSn/XENgiPnve9975Q+3hvB2972tmN9CoKDIOzJ8c+R2oAnn3ySr371q1x++eUVY3AOZW3Nsaa2tpbzzjvvWJ+GYBzCnhzfvBH+xPHw+z99+nSmT59+rE9DIChxpAMY16xZY3/84x+3bdu2R0ZG7PPPP9++5ZZb7C1btlTsl81m7Xe+8512KpWy8/m8ffHFF9ujo6P2b3/7W/vOO++0bdu2V69ebd900022bdv2ddddZ2/cuNG2bdv+7Gc/a69evdret2+f/YEPfMAuFov28PCwfdFFF9mGYRzpqQsEgimGsCcCgWCyEPZEIBAIJocjLj0944wz/HUk1dXV5PN5fzZOORs3bmTx4sUkEgnC4TDLli1jw4YNrFmzxh9Gfu6557J+/Xo0TaO7u9tv57py5UrWrFnD2rVrWbFiBcFgkLq6Otrb29mxY4f/HoZh0NXVhWEYR/pxBALBMWQq2RMQNkUgOJ4R9kQgEAgmhyMWiuVD2B944AHOO+88CoUC//zP/8z111/P3/3d35FMJhkaGqKurs5/XUNDA4ODgxXbFUVBlmWGhoYqWvE2NjYesG/5MTz6+vpYuXIlfX19R/pxBALBMWQq2RMQNkUgOJ4R9kQgEAgmh6Mej/H444/z4IMP8m//9m/85S9/Yc6cOcyaNYsf/vCHfP/73z+gzbdt20iShD2u2apt2xNuK78ffwyBQHBiIeyJQCCYLIQ9EQgEgqPjqLqePv300/zLv/wLP/7xj0kkElx00UXMmjULgIsuuohXX32V5uZmf0g5wMDAAI2NjTQ3N/tRN13XsW2bpqYmksmkv29/fz9NTU0HHKO/v5/GxsajOXWBQDDFEPZEIBBMFsKeCASCE4F77rmHD33oQ1x++eU89thj9Pb2cv3113Pttddy0003oWka4IyXuuKKK7jqqqv8cU26rnPzzTdzzTXXcN1117F///7Dfv8jForpdJp77rmHH/3oR9TU1ADwqU99ip6eHgDWrl3L3LlzWbp0KZs3byaVSpHNZtmwYQPLli3jnHPOYdWqVYDT8e6ss84iEAjQ2dnpDx597LHHWLFiBcuXL2f16tVomkZ/fz8DAwPMmTPnSE9dIBBMMYQ9EQgEk4WwJwKB4ETgL3/5C9u3b+f+++/nvvvu41vf+hbf+973uPbaa/mP//gP2tvbefDBB8nn83z729/mpz/9Kffffz/PPvssO3bs4H//93+pqqriP//zP/nEJz7Bvffee9jncMSlp4888gijo6N87nOf87ddfvnlfOYznyEajRKJRLjrrrsIh8PcfPPN3HDDDUiSxI033kgikeDSSy/l2Wef5ZprriEYDHL33XcDcNttt3HHHXdgWRZLly7l7LPPBuCqq67iuuuuQ5Ik7rzzTmRZjIAUCE4UhD0RCASThbAnAoHgROCMM87wG2h5jbnWrl3rj+ZZuXIlP/3pT7n22mt56KGHiMfjANTU1JBMJlmzZg2XXXYZ4DTmuv322w/7HCR7fIH9cUhXVxcrV67kiSeeoKOj41ifjkAgOM4RNkUgEEwWwp4IBMc/v1z1Ij/73YZJPeZH3n0af/WuUw5p3/vvv59169bxzDPPsGbNGgD27dvHF7/4RX71q1/5+23bto3Pfe5z/M///A+f/OQn+eIXv8j8+fMBOP/88/nDH/5AMBg85HMUYS+BQCAQCAQCgUAgmIJ4jbnuuOOOimZZ43N9e/bs4eabb+bee+8lEAhMSrOto+56KhAIBAKBQCAQCAQnKn/1rlMOOfs3mXiNue677z4SiQSRSIRCoUA4HPabaoEzhufGG2/knnvuYcGCBQB+Y6758+f7jbkCgcBhvb/IKAoEAoFAIBAIBALBFGKixlxnn302jz76KFBqqgXw5S9/mTvvvJNFixb5r5+oMdfhIjKKAoFAIBAIBAKBQDCFmKgx1913383tt9/O/fffT1tbG5dddhm7d+9m3bp1fO973/P3++hHP3rQxlyHgxCKAoFAIBAIBAKBQDCF+NCHPsSHPvShA7b/5Cc/qfh71qxZbNy4ccJj3HXXXUd1DqL0VCAQCAQCgUAgEAgEFQihKBAIBAKBQCAQCASCCoRQFAgEAoFAIBAIBAJBBUIoCgQCgUAgEAgEAoGgAiEUBQKBQCAQCAQCgUBQgRCKAoFAIBAIBAKBQCCoQAhFgUAgEAgEAoFAIBBUcFRzFO+55x7Wr1+PYRh88pOfZPHixXzxi1/ENE0aGxv5h3/4B4LBIA899BA/+9nPkGWZD33oQ1xxxRXous4tt9xCT08PiqJw1113MW3aNLZu3cqdd94JwLx58/jqV78KwH333ceqVauQJIlPf/rTnH/++Uf94QUCwdRB2BOBQDBZCHsiEAgEk4B9hKxZs8b++Mc/btu2bY+MjNjnn3++fcstt9iPPPKIbdu2/fd///f2L3/5SzubzdrvfOc77VQqZefzefviiy+2R0dH7d/+9rf2nXfeadu2ba9evdq+6aabbNu27euuu87euHGjbdu2/dnPftZevXq1vW/fPvsDH/iAXSwW7eHhYfuiiy6yDcPwz2X//v32SSedZO/fv/9IP45AIDiGTCV7YtvCpggExzPCnggEAsHkcMSlp2eccQbf/e53Aaiuriafz7N27VpWrlwJwMqVK1mzZg0bN25k8eLFJBIJwuEwy5YtY8OGDaxZs4aLLroIgHPPPZf169ejaRrd3d0sWbKk4hhr165lxYoVBINB6urqaG9vZ8eOHUerkQUCwRRB2BOBQDBZCHsiEAgEk8MRC0VFUYhGowA88MADnHfeeeTzeYLBIACNjY0MDg4yNDREXV2d/7qGhoYDtiuKgizLDA0NUVVV5e/7escQCAQnBsKeCASCyULYE4FAIJgcjrqZzeOPP86DDz7IHXfcgSRJ/nbbtivuy7dLkjTh9om2vdYxBALBiYWwJwKBYLIQ9kQgEAiOjqMSik8//TT/8i//wo9//GMSiQSRSIRCoQBAf38/TU1NNDc3MzQ05L9mYGCAxsZGmpub/aibruvYtk1TUxPJZNLf92DH6O/vp7Gx8WhOXSAQTDGEPREIBJOFsCcCgUBw9ByxUEyn09xzzz386Ec/oqamBoCzzz6bRx99FIDHHnuMFStWsHTpUjZv3kwqlSKbzbJhwwaWLVvGOeecw6pVqwB48sknOeusswgEAnR2drJu3bqKYyxfvpzVq1ejaRr9/f0MDAwwZ86co/zoAoFgqiDsiUAgmCyEPREIBILJ4YjHYzzyyCOMjo7yuc99zt929913c/vtt3P//ffT1tbGZZddRiAQ4Oabb+aGG25AkiRuvPFGEokEl156Kc8++yzXXHMNwWCQu+++G4DbbruNO+64A8uyWLp0KWeffTYAV111Fddddx2SJHHnnXciy2IEpEBwoiDsiUAgmCyEPREIBILJQbLHF9gfh3R1dbFy5UqeeOIJOjo6jvXpCASC4xxhUwQCwWQh7IlAIDheEWEvgUAgEAgEAoFAIBBUIISiQCAQCAQCgUAgEAgqEEJRIBAIBAKBQCAQCAQVCKEoEAgEAoFAIBAIBIIKhFAUCAQCgUAgEAgEAkEFQigKBAKBQCAQCAQCgaACIRQFAoFAIBAIBAKBQFCBEIoCgUAgEAgEAoFAIKhACEWBQCAQCAQCgUAgEFQghKJAIBAIBAKBQCAQCCoQQlEgEAgEAoFAIBAIBBUIoSgQCAQCgUAgEAgEggqEUBQIBAKBQCAQCAQCQQVHJRS3bdvGhRdeyC9+8QsAvv71r/PBD36Q66+/nuuvv57Vq1cD8NBDD3H55Zdz5ZVX8uCDDwKg6zo333wz11xzDddddx379+8HYOvWrVx99dVcffXVfOUrX/Hf67777uOKK67gyiuv5Kmnnjqa0xYIBFMQYU8EAsFkImyKQCAQHB3qkb4wl8vx9a9/nbe97W0V2775zW+yYMGCim0/+MEPePDBBwkEAlx22WVceOGFPPnkk1RVVXHvvffy1FNPce+99/JP//RPfPOb3+S2225jyZIl3HTTTTz11FN0dnbyyCOP8Ktf/YpMJsPVV1/Nueeei6IoR/fpBQLBlEDYE4FAMJkImyIQCARHzxFnFIPBID/+8Y9pamryt2Wz2QP227hxI4sXLyaRSBAOh1m2bBkbNmxgzZo1XHTRRQCce+65rF+/Hk3T6O7uZsmSJQCsXLmSNWvWsHbtWlasWEEwGKSuro729nZ27NhxpKcuEAimGMKeCASCyUTYFIFAIDh6jjijqKoqqlr58mw2yz//8z+TSqVobm7m9ttvZ2hoiLq6On+fhoYGBgcHK7YrioIsywwNDVFVVeXv29jYyODgIDU1NRMeY968eUd6+gKBYAoh7IlAIJhMhE0RCASCo+eIheJEXH311cyZM4dZs2bxwx/+kO9///ssXbq0Yh/btpEkCdu2D9g+0bby+/HHEAgEJy7CnggEgslE2BSBQCA4PCa16+lFF13ErFmz/Mevvvoqzc3NDA0N+fsMDAzQ2NhIc3Mzg4ODgLNo3LZtmpqaSCaT/r79/f00NTUdcIz+/n4aGxsn89QFAsEUQ9gTgUAwmQibIhAIBIfHpArFT33qU/T09ACwdu1a5s6dy9KlS9m8eTOpVIpsNsuGDRtYtmwZ55xzDqtWrQLgySef5KyzziIQCNDZ2cm6desAeOyxx1ixYgXLly9n9erVaJpGf38/AwMDzJkzZzJPXSAQTDGEPREIBJOJsCkCgUBweBxx6elLL73E3//939Pd3Y2qqjz66KNcc801fOYznyEajRKJRLjrrrsIh8PcfPPN3HDDDUiSxI033kgikeDSSy/l2Wef5ZprriEYDHL33XcDcNttt3HHHXdgWRZLly7l7LPPBuCqq67iuuuuQ5Ik7rzzTmRZjIAUCE4UhD0RCASTibApAoHgtSgaFiFVXKevh2SPL64/Dunq6mLlypU88cQTdHR0HOvTEQgExznCpggEgslC2BOBYOpgWDZjOZO8btFRGzzWpzPlmdRmNgKBQCAQCAQCgUAwlbBsm3TBJFOwsIFEWGQTDwUhFAUCgUAgEAgEAsEJh23b5DSLsbyJZUMkIBMPySTzBtWRY312Ux8hFAUCgUAgEAgEAsEJRUF3BKJu2gQViUREJle02DdawLahuUqUnr4eQigKBAKBQCAQCASCEwLdtBjLmRQMG0WG2qiMbtr0JDVMyyYeUogFRenpoSCEokAgEAgEAoFAIDiuMS2bVN4kq1lIElSFZcBmIK2jmzbhgEx9VGU0ZzCY0lg6PXCsT3nKI4SiQCAQCAQCgUAgOC6xbJtMwSJdMLGBWFBCkSVGczpFwyaoSjTEVFJ5g73DOrIELdWi7PRQEEJRIBAIBAKBQCAQHFfYtk1Ws0i5jWrCAYmQKpHMGeR1i4AiUR9VyRRN9o8UkSVoqgrSkAiQzhvH+vSPC4RQFAgEAoFAIBAIBMcFtm2TdxvVmBYEFYlIUCKVNxjJWu66RIV80aJr1BGIjYkgjQmVVN7kle4MOc1i+RyRVXw9hFAUCAQCgUAgEAgEUxrbtikYzjpE3bQJKBKxiES2YNI7ZiJLUBNRKeomPaMaEtCQCNCYCDCWM3m5O0tBtwgHZGY3R4/1xzkuEEJRIBAIBAKBQCAQTFmKhpNB1NxOplUhmaxu0jdmIgFVYQXdtOlLFgGojwdojAdI5g1e7spSNCyiQZm5zVFqYir9ae3YfqDjBCEUBQKBQCAQCAQCwZRDNx2BWNBtZAniIZmCbtKXdjKG8ZCCYVgMjGnYQF0sQEMiQDKr81J3Bt10xmHMbIwRDyt0Jwts7k1T0C3aqsPH+uNNeYRQFAgEAoFAIBAIBFMG3XRKTPO6hQREgzJFw2TAF4gyumEzmHL+rosHqI8HGMnovNyVwbBsqiIqc2pDhFSZfaN51u8rYFg2tVGVhS3xY/wJjw+EUBQIBAKBQCAQCATHnAMFokTRsBjKOIIwFpTRDYvBlI4kOWsQayMqQxmdl7rSmBbURFXaa8PY2OwZztObKmLb0JwIMqshSk1UzE88VOSjefG2bdu48MIL+cUvfgFAb28v119/Pddeey033XQTmubU/z700ENcfvnlXHnllTz44IMA6LrOzTffzDXXXMN1113H/v37Adi6dStXX301V199NV/5ylf897rvvvu44ooruPLKK3nqqaeO5rQFAsEURNgTgUAwmQibIhAcP+imzUjWoD+lU9AtIgEJWbYZyuhkCiaxgIwiwVBaJ10waUwEmdkQpqBZbO7K0JMsUh0JcHJ7jNp4gC19GZ7dlaQ/XWRabZgVc2o5dXq1EImHyRELxVwux9e//nXe9ra3+du+973vce211/If//EftLe38+CDD5LL5fjBD37AT3/6U37+859z3333kUwm+d///V+qqqr4z//8Tz7xiU9w7733AvDNb36T2267jV/96lckk0meeuop9u/fzyOPPMJ//Md/8KMf/YhvfvObmKZ59J9eIBBMCYQ9EQgEk4mwKQLB8YFRJhDzmkVYBUmyGc7qpPMGkYCMDAxldLJFk6ZEgI66MKm8zpbuLEMZjabqICdPixMMSmzoSvHC/hR53WRec4y3n1TPwtYEsVBlEWVOE9fooXDEQjEYDPLjH/+YpqYmf9vatWtZuXIlACtXrmTNmjVs3LiRxYsXk0gkCIfDLFu2jA0bNrBmzRouuugiAM4991zWr1+Ppml0d3ezZMmSimOsXbuWFStWEAwGqauro729nR07dhzN5xYIBFMIYU8EAsFkImyKQDC18TKIfa5ADKlgYzGSM8gUDcKqBDaMZHTyukVzVYCW6hDDGZ1Xe7NkCibttSEWtMUomBZ/2ZVka1+WoCJzSkcV582tY1ZDlIBSkjqWbdM9ludPO4d56OW+Y/jpjx+OeI2iqqqoauXL8/k8waAzvLKxsZHBwUGGhoaoq6vz92loaDhgu6IoyLLM0NAQVVVV/r7eMWpqaiY8xrx584709AUCwRRC2BOBQDCZCJsiEExNNMMiXbDI6xa2bRNSnTWIozkLCZuQIpPTLEaLBkFForUmiGXZ9I1pFHSLkCozoyGMqkh0JQu83JdBAlqqQ8yoi0xYWprVDHYN59g1nCOnm4RVmQXNopnNoTCpzWwkSfIf27ZdcV++XZKkCbdPtO21jiEQCE5chD0RCASTibApAsGxo2hYpAvOmAtsm4AqUdAtknkbWbIJKjLZgkWu6JSbttUEyGkW+4acTqWxkEJnU4S8brJzKEemaBJQJGY1RJhRFyEcUCrez7JtesYK7BzOOs1sgNZEiNM6qmmvDiOLa/SQOKpmNuOJRCIUCgUA+vv7aWpqorm5maGhIX+fgYEBGhsbaW5uZnBwEHAWjdu2TVNTE8lk0t/3YMfo7++nsbFxMk9dIBBMMYQ9EQgEk4mwKQLBm4tt2xR0i8G0zmDaoKhbqDLolsVY3sAwbQIy5IsWYzmDUECipdrJ+u8cyNObLJKIqMxsjCArsLE7zSt9WWRJ4uS2BG8/qZ55zfEKkZgpGmzqSfHQy308vXuE0ZzOwuYE713YzNvnNDCtJiJE4mEwqULx7LPP5tFHHwXgscceY8WKFSxdupTNmzeTSqXIZrNs2LCBZcuWcc4557Bq1SoAnnzySc466ywCgQCdnZ2sW7eu4hjLly9n9erVaJpGf38/AwMDzJkzZzJPXSAQTDGEPREIBJOJsCkCwZuDbdvkNYuBtMFQxkAzLGTJpmCYpAoGlmUjA5mCSSpvkggrNCYC5DWL3YN5xvI6LdVB2upCjBV0XuxK0Z0s0JwIsnxWDWfPrqWjNowiO4LPsGz2jOR4cscQD2/p5+X+NLWRACs663jfyS0saasiHhITAY+EI/7WXnrpJf7+7/+e7u5uVFXl0Ucf5dvf/ja33HIL999/P21tbVx22WUEAgFuvvlmbrjhBiRJ4sYbbySRSHDppZfy7LPPcs011xAMBrn77rsBuO2227jjjjuwLIulS5dy9tlnA3DVVVdx3XXXIUkSd955J7I8qRpXIBAcQ4Q9EQgEk4mwKQLBm49t2+Q0Zw2iYdlIOLecZmHZNqosYVs2Gd1ClqAupmLZMJTW0E2bcECmvS5EXrfYPZKnaFiEAzInNcXoqA0TVOWK9xrOaewazrFvNI9u2cSCCie3JOisjxILTixxDMtizZ4kf96T5JYLOt+sr+a4RbLHF9cfh3R1dbFy5UqeeOIJOjo6jvXpCASC4xxhUwQCwWQh7IngRMeybDJFi0zRxLKd8Ram5ZSd2q5ALGiOeAwokAirFHSLkayObUNVWCEcUhjN6QxlNGygIR5gel2ExniwYs1vTjPZM5Jj10iOdNFAkSWm1YTprIvRNG5fD9OyeKE7zVM7R9ifLGC6yuf7H1jwJn1Dxy8iDysQCAQCgUAgEAgOC8O0SRdNckUnYyhLTsZOM5yGNZIEBd2iYEMsKBNXFcZyOt2jRWQJaqMqBjb9KY3imEVQlZjZEKGjJkIsVFp3aFrOWItdwzn60k5jmsZYkAXNNUyviVSMwPCwbZuX+tL8cccIe0achjgAIVWiOapy5rTqN+trOq4RQlEgEAgEAoFAIBAcEkXDIlMwyeu22+UXNNPEMG0kwLYdgSgB8bCCadmM5nRMCyIBidqYSqposGskD0BDPMjC2jCNiaDfaMa2bQazGntGcuxP5tFMm2hAYWFzgln1URITrDm0bZvtQzke3zbEzmHnNQABRaI5HuCsGdUsaa06aOZRcCBCKAoEAoFAIBAIBIKDYts2ed0iU7DQTBvLtoFSeakE6IZTcqrKEA/JTkOblIYEJKKOYBzMaGgZjbAqM7sxSkdNmEiwlD1M5nX2jObYO5Inp5soskRHdZjOuijNidABAs+2bXYO53li+xDbh3IUDUccqjI0xlSWTatiWUfNASWsgkNDCEWBQCAQCAQCgUBwAKZlky1aZIsmhmVj44hBzbCwbZBwsocA4YBMUJFIFQzSBWfOYSwsk9FM9o0WkIDGRJBptWEayoRbVjPYO5pn70iOZMFAAlqqQixtq6KjOow6rrTUsm22DWT5444Rdo2UxKEiQ31U5bSOBMtn1NIYE+LwaBFCUSAQCAQCgUAgEPhohkWmaPkdS7FtNNPCtGxs2xGQhmkjSzaRgEzRsBnNOjNHw0EZW4LhnIaVg1hI4aSmGG01IX/moWZY7E/m2DOaYyCjAVAfDXB6RzXTayIVsxHBeb+X+9I8uXOEfaMFv6xUkaEhpnJaWxVnzaimMX5g1tHDsmx2Dud4sTvFi91pvvnuk97Ab/DEQAhFgUAgEAgEAoHgLY433iJTtNAMRyDatk3RsJxupraNZjhlp0FVIqhIpAsm2aKFIkNAhbGCyVjGIKBIdNSGaa8JUxVWkSQJ3bTYM5JjXzJPb6qAZUMipLK4JcGMugPXHeqmxcaeFE/tGqV7rIhulspKm+MBzphexZnTaqiNBg/6mTJFg009aTb2pNnUkyJdNJElmNsYe0O/yxMFIRQFAoFAIBBMeZymGaKMTCCYbAzTJqs5gs+wHHGou9lDy3Iycd5cxIAiUzBsUnkTsFFViaJhkcybSEBDIkh7TZimeBBZdsTh3tE8+5N5elxxGAnIzG2IMaMuSl0kUHFdFw2LtXtHeXZPkv6MhuFUtaLK0F4dZPn0as6YXnPQOYm2bbM/WfCzhtuHstg2JEIKS9uqWNqeYElrgtgEzXAEByK+JYFA8Ibg/NDYFQNyBQKB4FDxbEhBdzIaRcOmo/bgmQOBQHDo2LZzbWWKJgXdyR5atrP20LLAxkY3HNGoKjKyBLmiRdY2kWUJS7JJFwzsIiTCCvObY7TWhAmpMrppsX8sz75RJ3NouuJwTkOM6TURGsatHRzOaTy1c4RNPWmSecOfcxhQJKbXBFkxq5bTOqoP6k+kCgYv9abZ7N6SeQOAGbUR3reoiVPbq+isjyLLItB0uAihKBAIJgXPqSsaJafOthGOnUAgOCRs28laFHWbgmtDLMvGxmmY4XRZFAgER8P47KFlOffOYxvTdJrVyJKEIkHesMhrJjZgS5DVDSzbaVwzsz5Cq1taqpsWvakC+5J5esaKmLZNRJWZPYE4tGybXUNZntw5ws6hHFndaYwDjqCcXhvivFl1LGpJoEwg7gzLZsdglk29aTb3pNkzkscG4kGFk1sTLG5zsoa10cCb98WeoAihKBAIjohyp84Thqbr1IENNuiWBQihKBAIJsYwHftRMGyKulWyIbZzb5iuM2s72wQCweHjjLawybrZQ9Nysod6WXMawx15oUpO45i8YTrBGRlyhrNfQJHoqAnTWh2mJqpSNCy6UwVe6B6jL+2UlYZVmc76KNNrHXHozUUsGhYvdif5854kPami36lUAqrCCvObYlwwp47WqvCEJeb96SKbetK81Jvm5f4MBd1ClmBOQ4zLl7awuDXBrLqIyBpOMkIoCgSCQ2J8xlDzhKHtlKjYgFnm1HlzlQQCgcDDCS459qNgWBhmmQ2xwbA8JxZsy3EkvQYWthCKAsFhoRlO11JvtIXTqdTCtJysnnP92W7GHgq6iWnbSG4m0bBsFFmiORGktTpMfTxATjPpGiuwvifJUEbDBmJBhbkNMTqqIzTEHXFo2zZDmSJP707yUl+a0bzhrzeUJacZzekdVZzXWTfhesF00eCV/gwv92XY3JP2O6M2xoKcPbOWxa0JFrXEiQaVA147EZZts280z5a+DC/3ptnSl+Ynf3XK5HzRJzBCKAoEggmx3e5m5cLQW8Ngu897EUhvPYMXlfRer4rInkDwlsW2bUwLv+Kg6ApDL5BkY/vOq2U5VQiOWCyzIYqEKruZR3dWm0AgODim5XQuzWkWRTcTaJgmhtuUpvx6Q4K8ZmK416NmOtekLEFj3BGHDfEAac2kO5lnXXeSZF4HoDqssqglQXt1mFq3IU3RsNiwf4xn9yXpThYpuN1SAUKKxIz6EOfMrOW0jioUuXK9YV43eXUg6wi5vjT7RgvYOBnKBc1x3rWgkcWtCVoShzYbUTMstg9mebnPEYVb+jJkNROA2miAhS3xyfzaT1gmVSi+9NJL/J//83+YMWMGACeddBIf//jH+eIXv4hpmjQ2NvIP//APBINBHnroIX72s58hyzIf+tCHuOKKK9B1nVtuuYWenh4UReGuu+5i2rRpbN26lTvvvBOAefPm8dWvfnUyT1sgEIC7iL1URqq5i9hNJ9zvl6nYdmmtkGXhZhW9ltWVTl3hKM5H2BOB4PjCKUd3haHuNMUorzBwmmM4jqPTdt9xXL3nwbEhsuQ4ebppY2vORG9VkZAOLXFwUIRNEZyoeI1psppJXrP89YalzqW2X24qIVHQHXFoebMRbSfL15gI0lIVoi4aYCSv053K83zXKBlXYDXEgpzSVkVHTYRESMW2bXpTBR7YNswrAxnG8ia6VSoprYmoLGiO8fbZTklpOZppsWMw5wi5/gy7hnKYtmMD5jZGuXxpCwtb4nTWRw8p6JzM62ztz/BKf4YtfWm2DWT9oNO0mjDndtaxqDXBwpY4LYmDz1oUVDKpQjGXy3HxxRfz5S9/2d926623cu2113LJJZdwzz338OCDD3LZZZfxgx/8gAcffJBAIMBll13GhRdeyJNPPklVVRX33nsvTz31FPfeey//9E//xDe/+U1uu+02lixZwk033cRTTz3F+eefP5mnLhC8pXAEoOOMeVlDzSiJQBvbb4dtWSVhaJqlbCLgLjL3foyc7RagKmDLzg/BkSLsiUAwtfHK0csDTKblBJdsVwjqpisMrQlsiASKW6JWLLM/SI5tsSSLvCsYAY7WrxM2RXAi4V1/Xmmp7jahGS8OTcspJdV0C92yMXHEoWU711lTVYjmqhCxkMJApsiO4Sx9e4pO2akEzYkQC5qdzGEkoJDTDNZ3jfH8/hR9ac2fsQgQVCQ66yKcPbOG0zqqCCilrKFh2ewZzvFyf4YtfRm2DWbRTefcOuujXLqwiUUtcU5qjL1ut3TDtNg9kmNrf5at/Rm2DmToSxUBR2jOaYzx/sXNLGxJsKA5TnVENLU5UiZVKGaz2QO2rV271o+urVy5kp/+9KfMmjWLxYsXk0gkAFi2bBkbNmxgzZo1XHbZZQCce+653H777WiaRnd3N0uWLPGPsWbNGmGEBYLDwLadkhLNFYQVDp1VahrhOXSew+ZF+p0fFOdYulla12C6223JaVxT0C28VUSqLFEdOXITI+yJQDC1KK860NzmM5aNLwxNq7T2yXbL0f0Akg2KK/R0t7zNK2WXZQkb97heX3wgFlJojAepjgSoiaoHDOM+XIRNEZwI6GZp3aFmOl1KdcvCNA8Uh0VXHBq2c2+7GbuWqhDNiSCSItGfLrKxZ4wRt6Q0GlCYWRelrSpEcyKEZdm83J/h39d1sz9ZIKuZ/lpDL2t4ckuc8zpraSnLGmqGxZa+DK8OZNg6kGXHYI6iGzyeVhNm5dx6FrbEmd/0+usMh7OaIwj7neNtH8z6tqI+GmB+c5xLFzaxoDnO7IYYITGWa9KY9Izi+vXr+fjHP04+n+czn/kM+XyeYNDpetjY2Mjg4CBDQ0PU1dX5r2toaDhgu6IoyLLM0NAQVVVV/r7eMQQCwcExrVKW0HPsSuVerkNXVvaFXSohtW0nci+5x9HMUgmq5D5hTCAKqyIqLVUhqiIq1ZEAkYBM0Ti6jKKwJwLBsaF8faFXdeBkB0vdjQ3T8isOSmWkpeY0siS5GY6yMnZXLGpmpSiMBmXqYkGqIyrVEZWqsIoNjBV0knmDbUNZknmdi05qPOLPJGyK4HjFML11hyYFd92hbh4oDgEKuoVuWRhuZ3KAkCrTUROiJqqiWzZ96SLP7hulYFhIQH0syJLWKtqqw1QFFXYM53hyxzA7h/OMFUwnOOyeSzQgM7chzNkzazm5NeGXheY0k43dKbYOZNk6kGHXcN4RrMC02jDnz6ljXlOM+c0xqsMHz/AVDYtdQ1m2DmR51c0WDrqNbLxs4aULm5jfnGB+c4zGeOiN+toFTLJQnD9/PjfeeCMrV65k9+7dfOxjH8MwDP95r1xtfOcy23VAJ9o+0TaBQFDCKiv/chy60qJ1p9GM7SxUL3fo3O3Ob4hz/VmuQ2fZXukYSLLbpbBM8HmisLkq5Dp0AYIqpIomybxOb7rAKwMZkgWdomFxzantR/S5hD0RCN48yqsOnDWGlmNHbM+OlLKFluUJScd5tNyZa14pnFm2tpkJbEg4IFMbDVAVCVAdUUmEFHTLJpnXSeZ1ulJ5kgWdTNH0XxNQJGpew7k8FIRNERxPeE1pskXT7RDsNIPyykrLG0Fppps5dCuFABJhhYZ4kKAqkSoadKXzbOp3soYBRaI1Eaa9OkxLIkh/Wue5/Un++6V+RvOG6ws45xGQJabXhFg2rZpl06qJu5n9VMHgha4xXh1wRN3e0Ty2Wzkwqz7KJfMbmNcU56SmGLGDZAx102LvSJ7tg1m2D2bZNphl70jOf+/GeJAFzXEuWxxnfnOc2Q3RinJWwRvPpArF2bNnM3v2bABmzZpFQ0MDvb29FAoFwuEw/f39NDU10dzczOrVq/3XDQwMcMopp9Dc3Mzg4CDz589H13Vs26apqYlkMunv6x1DIHgr4q8JMm10o1SqVRKFXqaw5ODZVmUZqdcGWzctLNxMIk6k3ysJ8wgHZKrc6H5VWCUaVNBNi1TRYKygs324yNg4h06RnJLTtqowNUexLkDYE4HgjcGbgVoKLlV2NTYPJgrLGllBaf2h6a57AvzB3R4hVXbsh5spjAQVCrrJWMFgNK+xe9TJFOplr0mEFGojAWbVRamJBAgpMqM5jf1jxaP63MKmCKY6hmmT1y1yRZO87ohD3csYemMtLBvTdmaPGpbTuMZtYEptVKU6GsCwLIZzOpv6UhhuVq8hFmRxa4LGWJCxgsHGnjRP7RphNGc4foR7CcoSNMQCLGlNsHxGDU3xILYN+8cK/GVvkh2DObYPZelPO1m+gCIxtyHGZSc3M785xuyGKGH1QGFoWjb7k3m2DziicPtQlt3DOX8NciKkMLcxxhmntDK3Mca8pjj1MTGH+VgzqULxwQcfJJfL8eEPf5jBwUGGh4f54Ac/yKOPPsr73/9+HnvsMVasWMHSpUu5/fbbSaVSKIrChg0buO2228hkMqxatYoVK1bw5JNPctZZZxEIBOjs7GTdunUsW7aMxx57jOuvv34yT1sgmJJ4HQS97n9F3aJYJgpNXxyWZQi9CL/r0Hmi0DAtN8oP4DSc8Zw8cH5gYiGF2miARFh1on8S5HSTsYJOb6bA1iGdbNH0y08kIBFWqY0EmVmrEg+p6IbFUE6jJ1Vg22CO0bzOHRfNPaLPL+yJQHD0lBpXeaXo7hpl2ytZe21R6OlCw+2MaHoBKKm0zSMWVKiJBpygUkjBxnZsSN6gK5Xn5QGdfNmIi4AsURMJMLMuSnVYRZYkxgoGvakirwxkGczoZIqG6yg7jubKuQ1H/F0ImyKYanhBm7xmT5g5NNzGNGZZ1lC3KpvHNCYCKIpM3jDoyxTZmcwBEA86aw2bogGSRYOX+zOs3TdG0s0YmmXCsDYSYGFzjLNm1DCtJkxOM9kxlONPO0fYPpRj11COglsVUBVWmdsY5R1z6jmpKUZnXQR1XJbPtJxuqDvcLOGOwSw7hnJ+ZUEkIDOnMcZ7FzVzUlOMOY0x0Yl0iiLZk1gnMTY2xt/93d+Ry+XQNI1Pf/rTLFiwgC996UsUi0Xa2tq46667CAQCrFq1in/9139FkiSuu+463ve+92GaJrfffjt79uwhGAxy991309rayo4dO7jjjjuwLIulS5dy6623VrxvV1cXK1eu5IknnqCjo2OyPo5A8KbhiULdtPxModdwxheFbkmJZTuDqL3SUS9b6KwpdMrFvNIvRzRWRvgVWSIRUqgKq8RCCrIsYVgW6aIjCr0M4XhBWB1WqQqpKLJMumAwktfpSxUZyGpkiiZFd72S0ySn9Nm+/4EFR/SdHCt7AsKmCI5fvDWBXhl60bD9VvkT2RF/uH2ZKNS8wJJl461E1svWKEkSJEKlKgNFcTKJabfSYKxg+PPKwClFqwoHqA6rJMIqummTKhgMZYr0pjVG8jo5t6W/6YrCcsdEAuIhhcZYgL89f9YRfzfCRxFMBUrdSk2yRWfWobfe0HCzhqbtrEHUTMvP0ntZw0RYJaBKaJZFMq+TKjrl0wFZojkRoj4WJJnT2DaYZ18yT9rriFomDOuiARY0xThzejXtVWH6MkV2DOYcUTeUo9ftICpLML02wtyGKHMaY8xtiNIYr5xjWDQs9o7k2DmUY9dwjl1DWXaP5H1RGFJlOuujzG2M+bf2mjDymywKi7pJV7LA/tG8cxvJc9slJ72p53A8MqlC8VghjLDgeML7kfBKSDW9LMLvlo96awxt2xF/jiNXivDbtoXul6C4r4MKQehlCeMhhVBARpbAsKFgmKSLBqlxzpyE5/wphFSFvG6SLpqM5HSGsjqpgkFeLzmQ4505gIjqrF+sDjkOpGXBJ9427c34WicVYVMEUx2vk6hmOLakaJgU9VI3US9oZLtCsMKOWKUROLob2PHLTu1SCRo4GYt4WCUaUFBVCcttrZ8uGAcElWRPQIZUgopMXjdJaSbDWY3BrE66YLiNONxg1rheV6osUR1WaYwHqI86JaeeAB1Ia/SnNf7xCANPxxJhTwSWbVPUPXFoOkFhd6mH6WYQnXLwUpdSf42gIhEOyli2TUpzfrvBuV4aYs51MpTT2TuaZyDjBF28DqfgXJf1sQCLmuOc0pYgElDYM5Jn13CO3SN59pSJukRIYU5DjLmNUeY0xOhsiFSUkaYKOruGcuwcdrKMu4ZzdCXz/rlGgwqd9VFm10eZ1RBldkOMGbURd5TWG09eM+keK9A1mqdrNE93skBXMk93Ms+AWyrr0RAP8t+fOutNOa/jmUktPRUIBJVYtrOW0GkS4XYPdIfWm15JSXmDGTfqZ/sR/8oMoZcl9JpIeEQCMomgQlB1upKatk3BsEgXDfpGChXrf5yMoko8qPjO3FjBYCRrkCo65SX+InmbCqcRQJUhElCIB2WiAQXLhrxukswbDKY1etx1RN6A7eNRKAoEUwmvA6lmOlmGol424say/MCN33hmnB2xxtkRrwrBa3oBjjMZCyqEgwqKjF+entVMejL5ipJRJ8OnEg3IBBWZrGaRKuqM5HTGCjknoFRxTpWfJ6xK1IadRjaxoILt2pCRnOHMcRusHGMRDcg0xII0xITLIjh+MNysodet1BGDXmmpU7pddDOG5eWkNjahgIzkLv8YzRuQd367ayNOdc9QVmfvSIH1XWknWFR2kQUUiY7qECc3x5lRFyGrmewedtYGPrp1iJwbIA4oEjNrI5w/u47O+ghzGmM0u9lC07LpGSuwbt8Yu4cdQbh7OOd3HwVnzWNnfZSzZ9XSWR+lsyH6ppSPZouGLwC7RvN0JQt0J/N0jRYYzlaKwdpogI6aCKdNq6GjNsI099ZRG3ndkRwCB2F1BYJJoKJ01CyVjhqm1y2w5DR5WcLy+WITO3KVJZzgRPcjARlFkZEkp5tp0bAYK+r0ZAsV+4YViYAqoyoypu1kB5N5g0zRIO87mZ5DWflZFEkipEpEAgoh1Ylk5oomyYLBaNbw97NsG0WWiAVkVFkmEpCQDZtM0ahocCMQCA4Ny3bshm6WZp4WjdIA7dL8QbBM6wC7YnjNZV7DjkQCEkFVRlYkbNspNc3pJj3ZAka6zOGUXXsjSQRkmXTRGVWRKppuC35nXeP4LCQ45aaRgEw8qBIJKEiS07Y/VTAYzGj0ljWmsW3baZoVVKmLqDS4zTjyukUqrzOYdhxBgWAqY9nOGJm8mzUsGha64WYLXZGoGRZaWXdS73dUVSVkGbK6s04RDWRsQqoK2AykdYZyutvgpvKajgUV5tSEmNsYI6jIDGc1dg/n+d2WQcbc7KMiQUdNhLOmV/uirr06jCLBUFZjz0ieZ3aOsGckx96RPPuTeb/JjOy+dlFLwn9tZ330DRtib9s2ozmdnrECvWMFRxSWZQdHc3rF/vWxIB21Yc6aVUtHTZj2GkcMtteEiR3l7FWBEIoCwWHhNYbwhs47gtBZV+iJv1KTGa8ErNQgwltvaLgZxPLofvl7BFWJkCojyW6HUsuiYJiMFUysCn+pNNqioFukNZOM2y1NK3Miy8tEPXEqS47wjKoyquxEELOaSapgYLhZT8/RDKsyAUUCGyS7NMvJYxSnDKYpHqQlEaK5rYqmRIimuOhYJhBMhJcl9MrQndJRp6S8lCW0y6oPxgnC8XbEcsrPy48fUB1B6NgRp2S0YFgks6VyUcuyCCiy2wnZsQFpzSSrOWLQOIgYdEZGOOuP4qpCOKCAW8kwVjAYzup+AMq2bRTZKUuNBBQaYqpbNutUPYxkDZK5ykxAVVilORFiVkOU5TNraU6EaEqIeWmCqYO3jCSvOyMs8prp+gaWe11bpVLSMmFo2DayjNMwzjCd9Ye6M07GsGzGigYjOWdpiFOeWnpPRXbKSNuqQsSDKgXdpDtVZFN3mmd3J8E5LK3VIRa3ucKuPsL02ggF3WTPSJ69I3ke3tznPB7N+xlGcI49sy7KKe1VzKiLMLMuyrTayKQPsM8WDV8I9o4V6RnLu/cF+sYKFf4FOGMyOmojnDu7nvaaMB2uEGyvEZnBNxohFAWCCfDElC8I3XIvzSy1qC4XhL5Th7PdsErrcPzov21XiDUbCKgSquKION1yhGdeN7HdgJk37sJGwjBNcprjyOV1k4JpM1F5VylL6ThniiyhSBLgiMlM0SybjebsG1QkFNmZE6YZTnah3CnMFt1mEvEQ0+MRmuJBGuMhGuNBGuNBmuIhaqMBZAmymslIVmMoox1QBiIQvBVxuheWys8Lbidjv/mUZZXWG5ulLsZGmYNplYnC8XZEVSSUAMg4jmbBcESeZ0f8CgZANywyuklWsygcQkAJ2yaoyIQUCVWpDCj5TXBw7E1YlQkHnMBTVVCmaDj2JlM0sIHhsrK1mohKYzxEZ32Ut80M0ZwI0ZwIugGmkHD+BFMSw7Qp6CaZokVOM9AMVxi6o6o002lMY3hr+d1rVpJLS0L8eaOW7TSRKxpktdKaRS/A4pR4y9SGA1RFVDTDYiCtsWswx6v9Tnl2QJGYVhPmjOnVTK+NML02TDSgMJTV2D+aZ/tAhj9uG2T/aIFkvpSJi7kdUd8xp94XhNPrIiQmKQOnGRZ9KUcE9o4V6HFvve7NW2fpEQ0qtFaHmVYb4cyZNbRVh2l1b23VYScQJTgmCKEoeEvjlYwa3tqfshbyhr/Wp9Qt0PIdqpITV94AwhODtttcxnIjh4okOYOn3ah+0f2xMIu2Kz6dYxZN2xGBRmlRuyf6vDVH5Zk+CZAlCTfZR0E3yemWn8203dcEFAlJcuYrFg0TbVwtWkFy1hs0JsKO8Es44s8TgnXRIEXDZCijMZLVGc46InDvUJYR9/FwVmckq1UM1ga4aIGYKSY48fGqDcoFYdG00PysnGtLygZmew1dDMvybUh5cMkXYbbT1liRQZIlzLLsIIBRtHzH07RsCqZT/pZ3g1sTZQW9kjfLco6ryhKqJPl2xAsoeSLQtm0iAYWga0uCMm7gyfCbaKXd7yKkyjTGg7RUhQ4IKDXEgzTEghUZCsu2SeUNRrIag+kir/ZlGM5qjLg3z7aM5DR+d+Pb3rR/U8FbGy/rX3ADK1nNRNMtf52w8xvtrjE0LUzcoIwzmdh5zr1Oi4btNohzgsG6RYUotG3bGRcTUogFFTTTZiSrsW+kwD6cZSXxkMLM2ggXzWugoyZMWJUpGibdY0X2j+bZ1D1GV7IyGxcLKkyrCbNsWjUz6iK+KKyLBo54LaFt26QLBv3pIv0p95Yu0p8q+NuGMlpFH4WAItFc5Yi+BS3xChHYWh2mKqyK0RhTFCEUBW8J/HU/btTPW0OoeyMdbKc1tT/eoSzSb9oHZgatMgfOdEuwJFesGZbtRumdY2im5YvBgmFRMC23U6F77DLx6TmH5Y6iZzpNyylxKT3vnK8kOWWkpvtjNX6tkCxBJBCgIRakPhakPhagPuY4btVhFVWSMSyL0ZzuO2ddwzk27hvz/07m9AM6nILTpts75uK2BHX+ewSpjwepi74xaxgEgmNFecmoJwQ9W2J4zWXMkjAzzYltiWlXXu8mThMLz1dySkVtX0Rqhu3aEleEumsXNa/xVJkQLK8qsOzSOiNZktx1VI6TW/7+tps59CsLTMtvSuORKRrOzLVogMZ4iLmNQVcEOgKwyRWEXlYip5mM5nRGck6AacdAhud2VwabHAGoV7yPR0iVXZsSYFpdhKUd1W/4v6/grYsXOM5pJpmCU7mj6aYb9DH9323dXwvsBJRtnOu0aDpVOzndCdJkNcvNMJYa0HnXpipD2F32kdNMhvNOqfZgxllP2FodprM+yunTAoTdoMpYXqd7rMCT2wfpSxUrfusb40Gm1YR55/xGp1lLTZiOmjC1kcMXhLppMZAuF4Cl+wFXDJY3twKnKqmpKkxzIsQZM2tpqQpVCMGGePBNH4chmByEUBScMJSaQFhohiPQim70z7TAME23TXxpjY9ZfvNEIN78sLIugoCzQM9ZM1jUDXRXBHoOo+47cu74C6vUlr58oHW5CAT3x8l9nTXOeZRwhKA3G208QUWiLhr0RWBNJEA04KwnlCXH4dMNi2TeIJnTGc0U2TOQYTSnM5rX0cZl/8ApVa2PBaiLOhmBha0J//h1sQD18SD10SB1sSDBSV63IBBMBSqyg74dsd15Z+4IG9MdAl9WZlbqFlxZKurZEM+e4AeVrAqbobnZfs10qgs0o9S4wpuH6tuHipJzx1bYdqmhlif+vP2dEnTHfnlVDeMxVZv6aID6WKgiqFQfC1IfDRAPqchAumg4NiSnM5pzKgs27kv6diWZ0xjN6RPaLEdoOseuiwWZ3RjzbUtdtMzOxIJEg4rIMgjeMJyRFRY5zSKrGeSLpttR2Ll5QV/NDdaYtu1fswXTIq+Z5HSbvOGUcftZfbuyL4EqO82gnMy57jeikSRoijnr+jvroyiSc/2mCgZ9qSKv9qUqqn9UWaKtOsysuijnza53xaAjCiOHWJpZ0E0GM07mfijjLBEZzBT9bX2pIiNZ7YDAcF00QHNViJn1Uc6aVUdzlVsu7t7XHEWGUjC1EUJRcNzgOT1e5qyouw6V4S74NjyHjYq5RH4E38IVgZbfBMJz4GxXIHqRdM2w0axSmajjIHqlJLabCXSyhJ7zVi7ynDVH+GWrplVy2jwHDiZuHQ/OD0hNOEBtVCURcmaYhVUn2i/hvLdmOGVfozmdvYNZXtybJFM0DjwYTtlHbTRIbTRAbTTArIYotdEgNe7fDbGgnw2siqgi8ic44fHXIbvir+Bm2by5hIZlYrot7E03CGXajrjzRaBloY+zJaXyc6f1vVa2dqmou0Ely/bXI5nl2UbrwGBSueAz3CqE8VUHsiQhuQGu8WXlHtVl2X8v8FMdVgm5WQ0Jx4lM5h2bkszpbOtNlwlCnbw+cSfjoCpTV2ZL5jTGqI0GqIkEXJsTdANMAaoigTdtpppA4OE3ntGc382cZlJ0y7P969RyxlSUC8KsYVLwBKFeCux4wdvS0hAbCRsJCcPtN1D27tSEVeIhlbpoBMl25hmP5HR2DmXY2l+6ZoOKRGtVmNbqEKdPq3YyclUh2qrD1MeCB712LNsmmdMdwZcpMpQuCcChTJHBtMZgRpvQR4gGFacyIBFi+azaChHYUhWmMRGa9GY2guMHIRQFUwZv/Z1hlbeGd0s3DNBNE9ONvJu2JwRLg6QN19kyLKsUvffuLcsVfiZFE3TDudcMd62A67Q5QpCKNUVWWXTQyxpY5eMtoCJyb5etLRr/+QKyRDyoEAsFiAQUVDfKb7tOouNMOuuDRtMF9g4YE5Z8SkB1RKXGFX9zm2K+Q+aJwfLHIjIveKtRHljSyu2JXupIaFhugKksG+itGzQtCwOvuUTJlpieLfEcS9NEM3BK00x3vXOZrfCzjNaBwSTTLo3GKV9/7DeScT6JX5Y+HlWWXEHmDLmPBhVCiiP8nBJS53M43UVNUnmdfYNZNu8bI1XQDygf81AkfNtSGw3QVhOusClegKk24gSbIgFZ2BfBlMEThQXNbaZU0Mnqzm9rUXcyhEXLyxg6HbxzhkVBdzqX5t3Zx7pb+q2bJTFouSWk2LY/1N4L3AQV2ZlFGpCJqhJFwyJV0EkXTUbLGruFVJnWqhDTaiKcNcNt3FIVpq3ayeSXB2p103JKtTMa2wYy7rpdp3R7NKsxlNUYTDtl3MY4IyFLUOcuM+mojXDqtGoaE06ZuFcy3pgIEg0KKSA4OOJ/h+BNo1TOZaGZOLX/Zql5jNdWWjPLh0h74yRK2cHyWYO6We60lWaO6VZpFplRJv6845WXhBq25XcutSqctYmctvKS0VKL+LCiEFElgopSiu7bNrrhdCDMuXOVAIYn+G6CqkxNRKU6EqAqHKCtOkxVxInIV0dUqiIBN2LvOGpVkQCqiMoL3sKUZ9l0E3/dnWaWZwbd0vOy6oKJ7InlrjVySj5NPwtY9NYTuw1ndMOpRjDKSsy8zsGWV57mC8PyCoKSLbFtZy3iRMLPsy3RgEx1OEA0KBNWFQKKhOquHTQtp6Og03DGIJkusncgM2GZp0ci7AzprooEaIiH6GyIUV1mW6rDakVgKR4WVQWC4wPbLR/NaCbpvE6m6JSEFgxHAGqmIwoLuknesMjqBjndq0iy/GUiumH5Gf7K8VYHNpDzqntk22lQky0bLzGEOyoqEaQ54YyocDr6lm5VYYVUwWDEbdI0nNV4uTvFn7Zp7nreUgOn8d1BParCqr9+95Rp1aW1wolSR/LaWFD4CYKjRghFwaTgOTDOQNlSswRNNynoTqZONx3BZnqZO8vGcDODphu5181SaYeT/SutC3IG2ptlJaBloyhMN6NYlgW0ykrEyrN83o+Ac97jsoDueh8vIi9LEgog295nc9YijHfJ0pTaTodV2XfC6quCruBzRGB1OEB1VHXuI07L65pIQLR+FgjKOFAEug0l3MYSmuGUi3kC0LC8WWUlu2LaXiOpkj0pGCXn0B+AbeJmF0tZA08I+usC/XWHlYEkPwtIZfdC5770vCo7c1GDikRAkf0qAm9dtWY6jmyu6MxUSwF9bqfDchQJEmHHtlRHArTXRFgYUakq2+YEmxy7UhVRSYRFUElw/OOMbrLJagZjeZ10znTWFRoWRcMko5tOExnDKSfN6abfxdxZ71tqUuddy07TqfJ5xo7dkSXnWjMtb5xF5bkUDYn6WJD26rDbITxILKgScUfDWLbNmNsXIJnX2bx/jKfzTjn3WN65TRTXCauy0wQuFmRmnZMB9Nfvxkq9AWqjAdEfQPCmIYSi4KCUl4Jqpu2UaxpOw4WCbqPppei9YZdKuXTL9Bu8eOtxnJEMlKL9lu03fzHcRjO6L/bc8lELLNsq697nROL99X6WKwYpOWW4+zh/e+t3QJIkt9mD7c80O3j83XPIVKJhlaqws04wES67hQ58XBV2BGHoBBF93lpQr0xP98qAvSywu003bf/fVS9/3v37I+fMONYfRTBF8AWgZbnZdqdRTFFzg0Ju23jdNNEN27cruu1mBtzAkWY6NqZQ8f/RtSemM4bGcIWkfoBDWAoiVZSMU769tG28bcHGDSI5Y28k8G2LEyQzJ8wWekhAvMxu1McmtifxkGt7ym7xkMj0CU5cvN/ngu504R7L6owVdTKakz3PuEIwp1nkDaeM1BsBo7s9A7xAjxc08n2HMiHoKb/xpZrgdCOtjgRIhFViQYWIm9FX3I7BummT0wySeYPdAxle2KuTPkjWD5zMX427XndaXYTFkSpqogFX9AX83gB1bvMmgWCqIYTiWwjP8SkfE1Fw2zh7tftFw6LoiraCabilnKbvpHnNXQpuhz7dLLVtN0ynAYyX2SufOVje3MWybGzLaR7jGfHxAo9xYs9bE1Bm418TCYgEFeLhALGgQjykEgspxELlwm5i4VcVVokE3rg1fd4gbd0XV5WCzDBL4qtCqPmCrVRSV3pu3H7GQV77etvKzue1nN3DQQjFExvbzbp5/5fzmjPsveDZFMuxL7oJBcO1KaZJ0Z0v5tkhT+h55ei65/CVZf+8knGvUsDP9JVlCWyrUvwdYE9csedk/twAEqVjvB4SEAspRAIq8ZBCLOjalqDi2pBAmdg7cFsspExZsef9W/prtv2mYKWlAd4acs9OGW6Dr8rH1gTHeP3jle/jLB+ofKybNv/16eXH+msSHAWW5QSHkjmdoXSR4ZxOsmiQLhqkNS9L6CzZKC8P9XwMwyzrIl62NGSiZnHjK4VCqlfKLSPLTlrfCxwXDCejP2ZYDI8VJzx3RXJEpCf85jbFqCn7e/y9WCIiOBE4roTit771LTZu3IgkSdx2220sWbLkWJ/SMcH7Mffaqec055YvmmR1i5xuOI6aW6NfcEsx8objsBX8mUD4P9jOCIlSJz6vWYtpOWUXpm1jW6WonL/OxgLfCXNOzn9cbqwPB1WWiAQVokGFeJnzFQ852yIBhUhQIRKQCakKIVUmpMgEVZmgIhNUnbJRyyp9vpLzU+m0FIommZzO/jLnqMJ5Kdvf2+47QhMcs9zZ8TIdlSLQnjCKORkEVWcsRkApvz9wWySgUBVWUd3v6mD7TbQtqMqoilT2XVc+F/Rep0r+81MVYU9K2K5zZdqguSIvq5nkik5pV1ZzZooVDGdYdN6w3KYPTmCp4M4S9MbE+KXlphc4wh00b5U6ENtO8Gh8Vs+2yjJ5vqgre0y583d4BFXZaSbl2pVYSCUeVIiGVD+gFB1374nAWMgRfiFVdstiyxrWePbT8spWS5/ftGyyBZ1kVqsYoeHbE+97co9R/jq/y/K49/Hsk1d6X7FP2bFNq2TXzHI7ddB93hjbVI4iS/6aS9W1Lars2JXKx04ZXyggE3cfq8rUdbqFPXFE4EhWp2+sQE9aYyRfZCxvkC6aZHXXT9G9LuJeNZHlBpCptA/uY89GlGf3y+2BN0LKC/kcbJQUQNa9DyiSGyh2BZ1bEVQeLK5yS7rHB5RF0ybBW43jRig+99xz7N27l/vvv58dO3Zw66238sADDxzr03pNPOdLMwzymk1Os0gXNVIFk3TBIFVwImgZt86+oJnkdUfEFQzLX1ztCRYvE+WXYrpOgrfWxVt07RlT3/kqy8h5ZVXe+bmby87ZfzTBtgP3k3Bas8vuej5VLt2Usu57iiS5JaDOqyQqHT5/zYA78N7QnDbtQ+ME2hslssYjS6CWOSeeU1P623VwFJmAuy0SUFDDKqosuYKqUmgFx4kvtfw5dbwwKz0/sQD0juGse1JkSfyAHQbHoz0Zj2VZ6IZB1rBJ5w2SeZ1UwV2/UzRJF931OkUnQp/XTTSrNK7By9T5jaN8AWeXCbjyoFB59N7NvpWJOL8awLYrRJ3Ha9mRieyNLEuokvN/W1Uk5zrzrgXZXevn2RbZKS+XcYbKl+yM7V8XXsDLa1ylFUxyOYN+264QcJ6dPZZiqhxJwrej3mf27JDzWC7ZKNdOKbJjb9RgaZ+Av39pH7XsWKos+9+1KpdslCpLBFT5kIWdb7sq9i+du3wCZlhOBHtSjm3b5IoGvak8+0YK9KQKDOZNUjndaQajuY1ijFKQ2RvdYpq2HwQq7/I7mYEfr7uol7WPuYEeL5jjZfnjoZLAq/IFoTMSRvxeCgSHxnEjFNesWcOFF14IwJw5c0ilUmQyGeLxuL/PGZ/9T6xQte9zOP2pAMnJhuEZBhtsz0bYZdtxHkuSMw0HX9jIgITtRa0kCcn9sXPuJe+lrvGRndf5h3WOKcleqZPkhcCQkJFlb18ZSXb2kBXZPx/Z21eSSgLtdYzc0djAiV978APKMq6z5jhtjjNQtm3cTZUk5PERZUX2nZmAKhOQnaxUQJYIqIp7LxFQFD8L5okrVRnn1IwTdAcTeuWOTmDcPieiMyMocSj2BODMm36FHarGCYk4M7IsvDVp+JdFSe9IB9gT71pHKrv2cWyIZ2dkWXai46Voilue6F77Mnh2BJz9vHW3SDKS7IgkZMf2SLKMLEmuefPsU7nRez0O/v//cGxL5b7SazxXvu3AJ8bv612rsheUKms+pVbYG1BcAeQNnPcehwOKY6fcY8hSmY0aL6Z8ISaXCbTSPl6wSCmzIcrrvK5cpL2W4BMzB6c+h2pP3nPHfxOI1SHLpd96ynwI3L8l75pWFGRZRlade8cVkV1/QvIf21C2Vh9M58ov2SjXd6hYxiFJpXW33mvLT9Z203WejzT++dKOB26ZYEdJqrz+ygOtFb/nMgTkykBqeYAioEgHL902bbI5g2zuwDWDklQKIsne2uKyAPbBnvP8L9n9G0qPpfLXjH+t7Hx93nMVfpDvJx1oe8rt1MGe9+ycLB9o14QAFkw2x41QHBoaYtGiRf7f9fX1DA4OVhjidF8fppI+FqeHb+g9R8e7WP2Ldvw2qbTvRNv8v8e9zt8uVwjR0ttXnofveJXdS+Pu8R1Oz8i4r/O2e86n+56SVHJGS0LWFVeShCTLrpF1fthkbz958ssQvczf+CzdgSK0TEyOywhWvqZcdI7bRy69vvyHLahW3kKKTKB8m7evWirZFMb82HIo9gQg1duLqaTexDObyC549+PtQ9n2g+4zzoa412K5DRlvMypswwQ2RPLshESFvZDc9/HvZUcYS7JrE9zHyK5t8GyG7NgGx14cup3wHa4yAebfK6Xn/ACUd10qMigSkmIjKxKyKqNKXpZygrJrtVQRoLpl1QHFLbNWSzYiqJQFuhTZKYn3yuMDzt+qMvk2UHDsOVR7smPrbkxl4Cjeadz1XX7NS3Lldll2frNl2RGUsgyy4lyLioqkysiKWhKjsuIIUkV2/y79fpcHmyrjYJJ3Vr49cF2HChElS5Jjbsqe9wSUNNF92THKPvkRU97bYHyWs6KTMROL3OMJWeIAgXlgJdQ4/6bc9zlg38N8flxFguc3VS5XcZa9BJVKP0kE6Kcmx41QtMddvc78usr/VNMXnoQcqXUicZKNggySjeJeOIpUnuVyHisSTnZLkghIzv6qhLuva6hsp7TC9td5uGWghukusLcwDNO9d8rBdMNCN0x/H8MwMd1GJaZlYZqmsw7QtA74bIeM5Dl7JYfQrvjxKPvb3y6XflTGi9g3yUJ6jqYklUpWZT865hgLxc0wKoqTUVS8ckxVJqAoqKriPpZRVAlVdZw8RZVRFYWAqjgW00nFYOFGXW0oaBa6afilZRM1Uihfh/hGlJpViMcJxOZEArR8P8cJLTmg4YBC2P07HFD8ba/1/FvZKB+KPQFomzsHKVaLJ8d8H0ySkN2yRudvkH3B5AT+ARRsJMlGAecmu3bFeVO3RbvXwMjE9ErOTc9uOI91z36496ZrO47OhjgnbjPeRhxoRyofl6odKh5PMhPZCanMRsiufVBkR4Ap7k317xUUVXYyiorrCCtOtYbk2j9bkjBs/DWHhkVZN9/Ja+jkochS6fr1ruEyQelf34HK54MH2T8UKD0XDiiEK9ZwK4SDzppkJ3v61r3e32gO1Z5E26djhaqR3OYqbq22/9sruX/bbpm3bVnYtoXttPjGstzHtuXs4z6PZWPbpnMelo1TQjWu9PtgJz/+2nZFpvPYFZeKgiQryKqKGgwQCAYIBoOEQwEi4QDhUIBQUCUcVAkEFGzJGTZf1N21zLozm/RwkCSc5nNut994uHRzSkzd58pv7n7V0YDfdOZwu5CXymTHz0AtW9LD+OY5B661LnVYLZW8G1ZpNmNpprPXF4KyJQATPV+53bTxS+Unet34bRM1lCpvQlXULTJFk9dtUuUe643AK3MPli3H8XsflG0/UHS+9mt8u1lmS737k1oSb8hnOZE4boRic3MzQ0ND/t8DAwM0NDRU7PPYne+mo6PjzT61o8ZyuwIWNINC0aCg6eQKOtm85twKOtmCRjankS1o5Ao6mbLH3n6ZvEY6p5HJFZ3X5ItkctohdfKTFRnZ/TGwpFIEUpIVUNx7N0KpKAqRcJBIOEAsEiQSVP11dKos+Y6yROkH1DDdDoi6iaabTsdUo+SQGabpzEQzLUzdaabjdTT0WtMfWsnca+M49Y6DqfqNVxSCAecWDihEQqrT3CISIBYOEI8EiYUDJCJB4tEgVdEA0VCAYMAVrIqMoiq+QypJkjsCoDQmwhsn4c2X9EZNVDxvlBrgaO6PbCqvl42iKHvOMClOMN/pcAgqrsMZKBOSrsMZDMiEx4nOcMBxZMMBucwRdb+zoFKxPVK2vTYWPOp/t8nmUOwJwB+/+d7jwqZ4NiRf1MkXDYqaQb7o2JFcwbULnp3IFcnkNceG+HajSNa9T+dKf+cKGrphvuZ7S5KEoirIigKSgiVJ2H4Gozyr4dgQWZEJBQKEQipBVSGgyu4aZtsX0LaNbwMMX0g7141pOkE4yzLcbsqejTh6+6B4QSrXLjiVAY5tCAUUIkGVcEglEgoQde1ENBwkGlaJhR07EQwqKG6WRpIkJNcmmDbuteuOBHHb+2tm5d+ZouHYSXe7Vrb/wZp0HApBVS5dm8HS41Cg/Jqd4Noe/1zZtR0NKkSDKlG30dhbNWN6qPZkwz9e/qbZk6JmkM4VSWWdWzpbIJkpMJTM0T+aZTCZpX8ky9BYjmSmQCpTJJUtkC/oFHUds+y6d/UOFmCAM+FTKheXZde4JyoDAcKhIIlElKbaGNOaEnS2VNPZmqCjPkYi6vgOFvhiMq+VbpmiQabg3soe9yULZIoG6YJBtmi87mUfUmVqogFHPLoC0ntcEw06s4yjQV9Y1rpzDGMh5zddcCDeGJNSo8BxgfbyILxV6vDuBeE006aoWxNst/wu+uU+km5687mdxmmpvDHBPqWxXYfzU7DxqyvfuC/qBOG4EYrnnHMO3//+97n66qvZsmULTU1NB5R1HK/IskwkJBMJBWCSgxu2bZMr6IxlC6SzRcYyBdK5ImOZovsjUvB/SFKZAqmcc58su6XSBbRxzmKu/A8JFEXFlp0fCEkpu8kKkqISDAWoq4rQUBOjozXOtKYqmmujNFaFaKoK01gVpqk6TGNViNi4zmKm5TT6KbhdGNN5nVROYyznOMDpvEY6p5PK645DXNDJ5nWyRZ1swXCaeWgGBc1w5rXphiu4nChnLm9gZDS8RRg2ZcL0CJ1PScIRoKpCKKgQDjhOZSSkEncFaCISpCoWpDoWpDEaIl7tidIgiUjA3S9IIuoJ1gDxcNDPDNi2XXIyvXEERmksQUE3/W1FvXJ7+X5Ft5tlxfO6RTqvO90s9bJxB65QPRx2fefSI/oO30hONHtSYUMmmUJRZyxbsgtjnl0o+3v8ttF0nmS6QCqbI1fQDzim13xekiVUVQVFxZZkcEvhHNuhIqmO/aiKhWltitHemKC9Pk5jVZjmasdmNFWHaUyEqI4GCCgymYJOMqMxliuSzGqkskXGcrorgDVSeY1MXieT18kWdHJF3enuqpkUNcMPZumGST5vYJia24n16OyCJOGssfZEWFAlGg4QC6vEw0Gqoo49aI8FqWsMUx0LkYgGSESDJCJBEtEgsVCAcEglGFAJBGRsW6q4lvO66TZFc679vGZS0CzfCfe25b393cepnH7APgX98K5zcMRoLOSIR+/eE5FeZ1lHYDsNRyJuR9loqHL/WEgl4nanjQTfuFFFk8VUtCehoEooqNJQEzui11uWRTJTYGQsz0gqx/BYjp6hNF0DKXqHM/SNZOgfyTCYzDKWLlAoahhGAXAuExOny2i2D/qATWVlsMglQakGgkRjYZrrq5g7rY6lsxqY3hhnSWucjoYo7fUx4uGJ7ZplOU26fEFZMEgVdFJ5g7GcTjKnkcobJHMaYzmdsZxO10iel7tTjOV0ctrBg2BBVaY+HqTeFY518SD18RB17t/+c+4t/hbqiCpJbvnpFBz76E8FcINyXpC96Pp7RaMUsC8epi/zVuW4EYqnnXYaixYt4uqrr0aSJL7yla8c61M6LpAkiVgkSCwShAMDnIdMvqgzksqTTOcZTeUZSeUZTbu3lHtL5xlM5hgayzKayjOWyZHNa4ATgUwBe8oPKjtrJJz1Eo5DKLuisjoepaEmSkt9nOlNCTrbapnRmKClJkxrbZTZrVWTOtjetp0IV9Z1Gp2bI0wzeZ2xnMZopkgyUySZLTKW0RjLF0nnnOxupuA6m0XdFbUGmmGSNWyyugEYfpmRXe5sHkGmNOhmNjzxmIg4YtNxLINl2x2hGXdFZ304QKyqJELj7utCgcMzA5blDD33nMxCWTS44IpKLypc0F87G3WsEPbk0AmHnNKy5rojc3x1wySZLjA8lmM4lWNkLMdIKs9QMstIKs/IWI5h9++hpLNPcjSPVVb3mcNxNjdDaY2VoiKpzr3s3odCAeqqYzTXxWhvrGJmcxXt9TE6mqtpq43QVheluTp82JkvzXCc0VzBIFvUHac0r5PMFBlOFxnNFEhmioxmioxlNVJeZjbvBat08q4IzemmYxMyOpAv6xLrdWE7dHugKrKb1Qz4YrI2HqImHqI66vztCc3maIBETZBEJOZvq3Kfr44GUcZ9J55NnEhketd4ruh0wMy6QjtbNMlpju3Mus/liiajWc3Z5j73Wg76eCSJiszlk7e9/ZBf+2ZxItoTWZapq4pSVxUF6g/pNYZhMpjMMTCaoWcwxfauEfb0Jtndm2Rf/xgDo1ky2QJFrYitWdiADowlYawbtm2C35VlJpFVN9AcpKE2xsz2ehbOaGBaY5wZjXFmNieY1RSnyQ0cHS6aYTGW10nldJKusBzN6oxkNIYzRUayGsMZjZGMxs6BLCNZjfxB/u96wrLJD36HaEqEaKoK+QHxpqoQ9fHgWzbz/mYgSaW15tHQsT6bEwPJPuIFclOHrq4uVq5cyRNPPHFclIm9lTBNi9F0niFXQA4lc65DmGVgNEv3UJq+4YzvNKYzBUzrIFEeT1iqKrIaIBIJUVMVpbEmRmtDghkt1XS21jCvo5bpjXHa66JEQ8c2FlLUTTJ5nXRBJ5M3yBR00nmdTMHJaKTdbWPZoitENcayBVI5jVROJ+2WHucKBpZtla3K94YK2xXb3FxjhZP9egRU2RebTlYjRJXrdPqOZKzs70jZftHS9lBg6kf+DxVhU44ttm0zlikwkso7AnMsx+BoloHRDAOjWQZGMvQOp+kdzjA4miWZzh90nWZJTAaQVBUlEKAmEaWpPk57QxUzW2uY21HLtPoYbbUR2uuiNFaF37A1fZZlk3UrI9J57zrX/es9ldcZy2oMpwqMpguMZoqMZh3hmXJLiHMFA8O0Km1BeSDKtt1V2QeunzsY0ZBKdSxEbSJEdSxETSxEVSxEjRuEqvK3BSvuq91b+DAyf14mKOeJzTIR6QvMCZ7LFA2+d/2pR/bFH0OEPTkQTTfoHc6wdc8gz77czaZdA+zpTTIwkiGTLaBrOrZ1kICCrLhZSRVJCRAIB2lpqOakaQ0snNnArJYEM5sSzGqOM70xTiQ4eX5ArmhUCMiRrMZw2rkfTBcZSBUYTBUZSBVJ5g6sppAlqIsHHTHpCsmmqhBN1WHaasK0VIdprYlQGwucML+nguOb4yajKDg+URSZhpqYW/7S+Lr727ZNKltkKOmsoRhK5hgczdA9lGZP7xj7B1P0D2cYHsuSSo6wb2CAfRMcx8s2hEJBquIR6mtitNYnmN5SxbyOOk6e1cgps5uorzr8KOThEHLXNh3t+3hzrVI5nVTeLaPJar6g9LZ5f49li4xmNcb8DIdTbueIzTIH0rYxsUkWLEbzeVS5gOI11LXd5k2GdUjrXAOq7GcxygXkb77yvqP67IK3HpIkUZOIUJOI0Nle97r7m6bFSCrvCMmRDANJR0wOjDi2Y/9Amr7hNEOjWcaGk/QN2fTthk3l76l6YtJp1lFbFaOpPs705mrmtNeycEY9i2bUM7MxTs1RrLuVZYmEm/GH6BEfJ68ZjGU1klmNsazGaNnjZNYpzU9mi4ymNYbTeT/jmco5wafxIrNgQT6l05fSUKS00wvMDToZpvm6ic6gKvuisdrNaDp/O/c18ZArRMN+1rMuEaYmHqKtJuY0IBO8ZQgGVGa01DCjpYaLl8+dcJ9c0eAvW3v544Z9rN/ex67uEYZGM+RzBWzDAC2PbefQcrBvZJB923bwOPilrSgBJCVAdXWMzvZ6Fs1q5OSZDcxtq2JOazUzm+IE1MPL7nlrkzvqXv/aLRomQ2nNFY4FBlJFX0QOpB1B+UpPiqF08YDGWSFVpqUmTFtNxKmk8kWkIyRba8LURIWYFLzxCKEomFJIkkR1PEx1PMzsjtcvd8kXdQZGnPUS+/vH2NGdZHfvKPsHUvR5gnI0yUDvAFsm8HRkVSUSCVOdiNBYG6e9qYrZbbUsmtnI0tmNzG6vJTEF6hckSSIWdtYqth6hc2nbTiYj5YpMp5zWdSbd+2RWq3iczGqMpAsksxqabuKVzJY3GLLd7EUgIGNIEqmiRUYrwGge+00eTi54a6IoMo21MRprYyzqbH7NfQ3DZCCZpXcoTe9Qmp6hFLt6kuzqGaXbDUSNjKXpGRmmZw+8WP5iSUJWAwRCIaoSEZrqEnQ0VTO7vZaFMxtYdlILC6bVEjxM5/NIiARVIkGVltrDtwemZfnBJuc6d2zASLrISKZYuncfD6cLjKQKjOU0vyNnpS1wAk5ZHYppJ9tCmcjUNKcr+GuRiASoiTsistYVkN5jR1iGqU2EuGLFSUf2hQmOO6IhlQuWTuOCpdMqtmuGxc6+NC/tH+XpLb0891I3u7pHyOdymLqObehgGmDksG2bZDbJhp5uNjyPU8usqEhqADkQpKm+mtkddSzubGLxrAZOaq9hbmsVjdXhoxZhIVWhvTZCe23kNfczLZuhdJG+ZIHesQK9yTy9yYLzd7LA2p3D9I8VD+jAHg6UxGRHXYSOuigdtRE66iN01EZpqgqJbseCo0YIRcFxTSQUYEZrLTNaazlr0bSD7mfbNkPJHFv3DbN59yCv7h9hd2+S3qE0g6MZRlNZ+vpH2PjygaUuiqqQiEVoqI3R1lhFZ1stJ89sYN70BqY1VzOtuZpoeOp19hyPJEnEwwHi4QBthxANLce2bfKa6ZTHus7l+MejWc1dw1kpNgWCqYSqKrQ1VNHWUPWa+xU1g74RZ63Vzp5Rtu4dZkf3KPv6k/QNp0kmxxjsHeDllytfJ8kKoUiI6qooLXUJprfUMH96PUs6GzltbjPTm6tRj3H2TJFl6uIh6uKHFwQzLYtkRqsUk+PuRyd4HtVE9cpjy9ZkqrKz/jASUFAVp+tsumgylkujdScpFA0yBQ29rOmEEIqCoCqzoKOaBR3VXPm2mYDzG9Uzmufl/Uk270vy0r5RNuwcYn//GGaxgFUsoFgGhlbE1A3sYgGzkKM3naR3z16eeQZXRDpVBZFohOktNSyc1cRZC9s5dXYjC6fVvCFVSIos0VztrLNcepB9TMtmMFWkb6xATzLvi8jeZJ6e0QJPbhlkMF2seE1QkWmrC9NRG3WFpCsm3ceNiZDISApeF7FGUSBwsW2b7uEsG7b3s3n3ENu6htndO0bfUJqRsSyFfBHLcKOV44hGQjTVJZjR6jiFs9trmd5Sw/TmGqY1V1NfHRUG+ThC2BTBoWAYJj1DKTbvHmLzrkFe2TvkBqBSjI5lyeUK2OaBwadoLEJDTZyOlhrmdtRxypxmTpndxMy2Wprr4iecrcgWdIZSRYbSBYZSBYZSRYZTBQZT3t/uNvf5TMGoeL2XtVRlGP3VR4/NhzgKhD05dgymCry4Z5QNu0d4cfcIL+weYSRTxNJ1FEzisoVVLDCWzpLPFcEywTZhfK8EWUFSg0TjETqaalkwq5GzFrRx1oJWFkyrPapy9MmioJl0j+bpGsnTNZpj/3De/TtH10jezfSXCKky7XURZjREmVEfY2ZDlBkNMWY0RGmvixAQTXcECKEoEBwy2YLB3qEMO3vTvLhrgK17h9ntlqslU1ksXcPSdSxdO6BzYSio0tFUzay2Wma01DC9uZoZbbXMaq1lVlsdDTVCSE4lhE0RTAa2bbOnP81zW3vZuGuQbfuH2dObpG8oRSqdwygWsc1KUaQoMvW1caY11zBvWj2LO5uY1V7LzFbnVh1/Y9dVTwUKmumIxzJhOZQqMJwq8JVrTjvWp3fYCHsydbBtm90DGTbsHuGF3aNs2D3MS/uS6KaNbZm0V4dojKlg6PQNjbGnZwStqDkC0ruV/75LMpIaIBaPMr2llpNnN3P+KdN5x9LpzGyeWkGfXNEoE5J5uoZz7B/Js284x96hLNliKailyBIdta6IdMWj93haXWRSu84LpjZCKAoEk4BmWOwbyrKjL8323hQv7x3mlb3D7OkdJZXOlQSku3bC0CuzkrFIkM62Wma21TGztYZZbXXMaqv1hWX4DZiPJzg4wqYI3mgsy6YvmWfLvlHWvtrL5l2D7OgapnsgRSaTcwNP2gGZjVg0xMzWWhbObGTutHrmdNQzu6OO2R311FcfeXMcwRuHsCdTm1zR4IU9I6zdPsTa7UOs2zlCtugEcKbVR1nQGqchqiDZJnv7xtiwrZfewZQjGk0TVbaxDQOzvHpAklBDYRrrqzhpWgNnLGznnWfM4qz5LQSnYOMm27YZymjsHcqydyjH3qEce9zHewazpMuy/JIErTVhZjTE6GyM0dkUY3ZTnNlNcVpr3riO0YJjgxCKAsEbzHC6yM7+NDv6nNvOvjTbesbY3TOKXii6DmGRIBaSqZHPFTCMynK1tsYqZrU5GYVZbiZyZlstne11J2Sp2rFG2BTBsWQsp7G9N82rPWNs2jPM5l2D7OwaYWAkhaVpzk13yufKqY6HmdNRz5xp9czpqGN2uyMi50yrd+fhCY4Fwp4cXximxUv7k75wXLtjmGF3/d/0hhgrFjSxdFo1UdVmV0+SF3YO8MKOQfb2JcF0so5hFbBMtGIRq6yRkyQrxBMxprfWcupJbbzjtOm8+6zZU7pSwLZtkjm9QjzuHcqyZzDHrsEMqXxJRIYDMrMaHeE4yxWQnU0xZjXGiB3jcWWCI0MIRYHgGKEbFnvdLOS2nhRbu8d4tSfF9t4UhaLjDNqGRly1icgWlq6RzeQZGctWHCcRDTlOoe8gOs7h3GkNIsNwhAibIpiK5IoGO/vTbO91bMbL+5Ns3NlPd3/SCThpRSRTRzJ0CvlCxWtrExFHNLp24qTpDcyb4WQlI6Ji4Q1F2JPjG9u22d6X5pmtAzy9ZYA/vzpIKu8EaRZ2VLNifhPnLmhifmuc7V2jbNgxwIs7B3lhxwC7epLOXFPLpDqiINkW+XyRQqFQUcIajoSZ1lLH0pNauXDZTC49aw6NtbFj9IkPHdu2Gc5o7BrIsnMgU3HfNZKrGPvRWhP2s4+dTTHmNMc5qSVBQ+LYd5YXHBwhFAWCKYZhWuwZzPJqT4pXexzxuLU7xc7+NIZpY1sWGBoNUYWaoE3ANinm8wyPZugeHMMqs8zjncPZHY6QnNNRR03itVt2v5URNkVwPJEp6GztTrGla4xXusd4pWuMLftHGR3LYmlFLE0jIpuotoFWKJBK53wfVZIkprdUM296AydNb2TeDKej80kzGmmqjYlqhUlA2JMTC9Oy2bR3lKdfGeDprQM8t32IomGhKhLLOutZubiFC05uYWFHNaOZIute7eO5V/t4bmsfz7/aTzJbBNsmGpSpjwexDINUJk8mk6tYsxwKh5jWUsvSua1cePosLlg2k2lN1cfNNVnUTfYM5dg1TkDuGsiSKZY+Z10syNwWRzTObYlzUmuCk5qPbl6tYPIQQlEgOE7QDYtdAxk/8/iqm4XcNZDxnb5ESGFmXZD6sERIMtELBUaSGXZ1j9A1kKL8cm+sifmZx3kznOzC/BmNzGytOebt+481wqYIjne8cQGecHyla4wt3WPs6Euj6yaWViRgG9SFIWDp5LN5+ofHKJQ5cLWJCCd5wtHNQM6b3sCsttq3vI04HIQ9ObHJaybrdg7xp1cGWP1yP5v3JQForYlwgSsaz1vQRCISwLJsdvQked4Vjs+92sfm3UP+jMT2+ig10QD5QpGR0SxjqUxFp/VgMMCMtjqWzW/nwmWzWH7yNGa11R434hEc2zSQKrK9P8O23jTb+zJs70+zrTdTISAbEyFOao0ztznBvFZHRM5tiZMIiwqINxMhFAWC45xs0eDV7jFe7hrjpf1JtuwfY0vXmL8YX5ZgdkuC+a0JmmIyUdnC1Ir0DaXY2TXMtn3D9A2n/eMFVIU5HXWOUzijgXnTG5k3s5GTptUTj741SkSETRGcqBR1k229KTbvTbJ5f5LN+0Z5ef8Yec3Etm2CmLQlFGqCIBkamXSW7oEkfcMZ/xiejVgwq4lFs5qc+85mOoWAnBBhT95a9Cfz/PHlfp7Y3MtTW/pJ5w1UReLMOQ2sXNzCypNbmNdW5Yu7XEFnw44B1m7t4y9benh2Sy8jaad0vLk2ykkdNUjYDIxk6OofI5vOYuulmYnhUID5s5o5/5QZnLt0BssWtNNSnzgmn/1osG2b3mSBbX2OeNzWl2ZbX4Yd/RnyWqlvQ2tNmJNaEixoS7CgvYoFbVXMaoyhiCY6bwhCKAoEJyCWZbNvKMtL+5O83DXGFve+azjn79NYFWLJjFqWzqhlTlOUiGQyNJLm1X2DvLp3iG37htjZPYJZthC/o6maeTOc7MJ8T0jOaDzhGuoImyJ4K2FaNjv702zem2TTvlFe2ucMLffWYamKRGdDlPYqleqAha1rDA2n2Lp3kN09o36lQiioMm96wzgB2cSMlhpk+a07k03Yk7cuumGxbtcwT2zu44nNfbzSPQbAzMYY7zq1jUtOaWfZ7PoKkWNZNlv3j/Dslh7+/FIPz27pYd+AE8xNRAKcMqeZptooY5kiO7tG2Nc7jKkVQC/NSaytinLa/HbOP8URjqfNb5/SDXNeC8uy6RrNs90Vjtt607zam2ZHfwbDzcSGVJmTWl3x2OaIx/ltCaoiIvt4tEyaUHz00Uf59re/TUtLCwBnn302f/M3f8PWrVu58847AZg3bx5f/epXAbjvvvtYtWoVkiTx6U9/mvPPP590Os3NN99MOp0mGo1y7733UlNTw7PPPst3vvMdFEXhvPPO48Ybb6x4b2GEBYJDI5nV2NI1xsv7HUdw075RtvWk/AXnjVUhls6o9QXkgvYqctk8W/cOsm3vIFv3DvHqvkG27R0iky/9KFXHw8yb0cDCmU3Mn9nIwllNzJ/ZREdT1RELSGFTBIJjh207wabN+0q2YuOeUUbcod3hgMzJ02s4ub2KxqiMbGgMDCV5Zc8gL+8aoGtgzD9WNBxw7MLMJhZ2NrFwVhMLZzUflX04XIQ9EUwFekZy/GFTL6s29vDMKwPopk19IsTFS1t51yltnLewmfAEMwr3D6b588s9rHm5hz+/3MPLe4cBCAcVzprfypz2WiRF4ZW9w2za3ksmncHWimCWylZnttay/ORpnHXyNJafPJ2TO5uO6woAzbDY2Z/hlZ4Ur/SkeaUnxdaeNCPZkm/SXhvxxeN89356fVSM8DgMJk0o/td//RdjY2N89KMfrdh+/fXX84UvfIElS5Zw00038cEPfpDOzk5uuukmfvWrX5HJZLj66qv5/e9/zw9/+EPC4TAf//jH+eUvf0lPTw9f+MIXuPTSS/nXf/1Xmpubufbaa/nGN77BnDlz/PcQRlggOHKyRYMt+5Ns3Jtk495RNu0dZXtvSTw2VYdZMr3GF49LZ9bSXB2mezDFtn1DbN0zyKt7B9m6d5BXdg8ymCx1Za2Khehf9eUjOi9hUwSCqYUjHnO8sGeEF/eM8OLuUTbuHfXLwmqiAU6ZWceps2o5qSVGRDLpHRjj5d39vLJ7gC27BugbKZWw1sTDnDy7mZNnt7BkTguL57SwqLPpDenCKuyJYKqRzus88VIfq17o4YmXeknnDaIhhXcsauGSU9u4aEkr1dGJG7qMpAv8+aVuntrczZ82dbF59xAA8UiAcxa1sWhmA8FgkFd7xvjzpv0MDY9h60UkvYjlNsyJhAKcuaiD5a5wPHNRx3E/Rsdb/zhePO4ayPg+TTykMr8twaL2Kr7ywUXH9oSPAyZtqEk2mz1gm6ZpdHd3s2TJEgBWrlzJmjVrGBwcZMWKFQSDQerq6mhvb2fHjh2sWbOGb33rWwBceOGF/M3f/A379++nurqa1tZWAM4//3zWrFlTYYQFAsGREwupnDGngTPmNPjbskWDl/cn2bR3lI17nfs/vtTnG9rWmginzqrl1Fl1nLa4k4+8ZxmxsGNOBkezbN0zwCt7BtmyZ+CIz0vYFIFgaiFJEjMaY8xojHHZGdMAp0vztt4UL+we5YXdI7ywe4Tv/f7VUnOOuiinzarj/e+ey9dn19NWFWTH/iFe3j3ASzv72Lyzn5///gWyboWCLEvM6ahn8ZwWlsxuZvGcFk6e3XLU2UdhTwRTjUQkwGVnTOOyM6ahGRbPvjrA71/sYdULPfxuQzcBReLti1p4/xkdXLy0jURZGWVdIsx73zab975tNgCDYzn+tMkRjas3dfHour0A1MZDnHtyO4s7FxIOh9jem+ZPm7ro6huhqBV45uVe/vTCbr8h3vwZjRVZx5Om1x9XZeOSJNFcHaa5OszbFzT52wuaybb+NK90p9nSnWJLd4oHnusSQvEQmDShmMvleOqpp/jTn/6Ebdt86Utfora2lqqqKn+fxsZGBgcHqampoa6uzt/e0NDA4OAgQ0ND/vaGhgYGBgYYHBw8YN/9+/dP1mkLBIIJiIVUzpzTwJkTiMeNexyHcMPuER55oQdwGubMa6vmtM46Tp1Zy2mdddywdOZRLS4XNkUgmPqoiszCjhoWdtTwVytmAY6teGlfkhf3OHZiw64RHl7fBTglq6fMrGPZ7Hrec+EyvvLJeupiAXb3jLJ5Zz+bd/SxaUcf61/p5jd/fMl/n7qqCCfPbubR7/3/jug8hT0RTGWCqszbF7Xw9kUt3HXNqby4Z4SH1nfx8Lou/rCpl5Aq846TW3j/sg4uWtpKfFznz8bqKJevmMvlK+YC0DOc4alNXY5w3NjFw3/ZBUBHY5wLT53OqZedjKwG2LhnhD9t7mFn1xC2VmD3UJ5df9jET3+3AXA6H5+1qIPli6dzzhJnvWP4OJy7Gg4qLJlWw5JpNf420zruW7S8KRyRUHzggQd44IEHKrZdeOGFfOYzn2H58uWsW7eOL3zhC9x3330V+3hVruOrXW3bRpKkiu0TbfM4kZpmCATHCxOJx+F0kRf2jPjC8Xfru/jl07sBiIYUls6o5b++8PbXPbawKQLBiUMspHLW3AbOmluyFX3JPOt2DvP8zmHW7RzmR3/Yxj+vcq7FzqY4y2bXc8aceq541+l8ubUKWZYYyxR4aVc/L5UJyENB2BPB8YwsS5zWWc9pnfXccfkSNuwe4aF1+3l4XTerXuwhHJBZubiV9y3r4MIlrcRCB7rybfVxrnnHfK55x3wA9vSN8cQL+/nDhr3815938NPHtiDLEqfPbeLyM2dwyrWnkSyYPLOlnyc399A3mMLWCpiKwV9e6WXVX7YDTsOqMxa0c+4pMzl36QzOWjTtuO2GLrqkHhpHJBSvvPJKrrzyyoM+v2zZMkZGRqitrSWZTPrb+/v7aWpqorm5md27d1dsb2xspLm5mcHBQRKJRMW2oaGhA/YVCATHnvpEiAsXt3LhYqfsyrZtdg9k/CzCht0jh3QcYVMEghOblpoI7zm9g/ec7qzRK+gmm/aOOsJxxzBPvNTHr9c45XJVkQCndzpZx7PmNHD9pacRncAZPhjCnghOFGRZYtnsepbNrufOK5fy/M5hHlrXxcPru/jdhm4iQYV3n9bO5WdNZ8WCJlRl4jLRmS3V3HBJNTdccjKGafH8q/088cJe/rBhH3//6+exLJuqaJC3L53GF947nzkddWzrTfPUS308s6UPNVrALhaoDlts7xnj2c1/4u6f2SiKzKkntXLu0pmce8oMzl4yg9pE5E3+lgRvJJNWevqDH/yAOXPmcPHFF7Nt2zbq6uoIBoN0dnaybt06li1bxmOPPcb111/PzJkz+clPfsJnPvMZRkdHGRgYYM6cOZxzzjmsWrWK//N//g+PPfYYK1asoKOjg0wmQ1dXFy0tLTz55JN8+9vfnqzTFggEk4gkSXQ2J+hsTnDF8hlHdSxhUwSCE5dwQClVKFzsBJn2DmZ5vizr+O2Ht2DbzniOJdNrWX5SA3dcseSI3k/YE8HxjixLfqb+ax9ayl+2D/Lfz+3noXVdPPiXfTRVh/nAmdO4Yvl0Tp5Wc9DMtqrIvG1hK29b2Mrtf7WckXSB1Rv38/iGffxhw14eWrMTgAXT67jkjFl88qZzCYWC/PmVAVa/1MtfXh1AVusIWRptcYmRTIEfPPgX/ulXf0aSJE7ubHIzjo54bKqNv5lfk2CSmbSup11dXdx6663Yto1hGNx2220sWbKEHTt2cMcdd2BZFkuXLuXWW28F4Oc//zkPP/wwkiTxuc99jre97W1ks1m+8IUvkEwmqaqq4h/+4R9IJBI8//zzvuF95zvfyQ033HDAe4uOYgLBiYWwKQLBW5uxnMbzO4dZu32ItduHeHHPKPt++MEjOpawJ4ITlaJu8vjmPh5cs5fHN/eimzbz2qq4Yvl0PnjWdNrrDr2TqW3bbOsa5dF1e1n1/B6efqkbw7SoiYW46PQZXHLmTFYs7mBr9xh/eLGHxzd2s70nhW1btMRkWuMyWi7L9r0D5ArOaI5FnU28/fRO3n5aJytOmXncznN8qzJpQvFYIoywQCCYTIRNEQimHgXdnHDG3FRH2BPBm8VoVuOh5/fz4F/28fzOYSQJzj6pkQ+dPZP3nN5+WCXcAKlckSde2M/vn9vNo+v2MJDMO5nN+S1ccsZMLjlzFrFIkCc29vL4xm6eeqmPbNFAlSVObo1SH7YYHR1j07Ze8kXdWX85r423n9bJO5Z1svzkaUTDE48AEUwNhFAUCASCcQibIhAIJgthTwTHgj0DGX6zdh8PrNnLnsEsiYjKB86YzjXnzuSUmbWH3XTJsmzWb+9n1fN7+P1zu3lh5yAA0xoTvPusWbzvbbM5Y14z63cO8/jGHv7wYjdb9icBaKsJs3RajAg6e7oGWf+Kk6kMBhTOWjSNd5zeyfmnzeKMhR0E1OMvGHQiI4SiQCAQjEPYFIFAMFkIeyI4lti2zZptQ/zHM7v53YZu8prJgvZqrj13Jpcvn05d/Mi6lvYMZ3h03R4eeW4PT7ywj3zRoDYe4pIzHdF44WnTSWY1Ht/Yw6MvdPHHTb3kigaxkMqKhY3MrAlQzGZ5/uV9vLi9D9u2iUWCnLt0Bhcsm81FZ85h/sxG0UX4GCOEokAgEIxD2BSBQDBZCHsimCqkcjr/9dw+/uOZPWzcO0pQlXnXKW1ce+5MzlvQjHyEIyNyBZ3HX9jHw2t28chzuxlJF4iEVFaeOp33Le/k0rNmEQsH+dPLffx+w35Wre+ieySHJMGyOQ2cv6CJ2pDFzr0DPLl+F9v2OZ2E25uquPCMOVx45hwuWNZJXdWhr7cUTA5CKAoEAsE4hE0RCASThbAngqnIy/uT/Oef9/Cbv+xjNKsxrT7Kh8/v5OpzZtJYdeQNZwzT4s8vdfM/a3bx8F920jWYQZElzlnUxvvOns17l89mWmOcTXtG+P2GLn6/vosXdg0DMKMxzsWntbNsVg3pZIo/rtvJk+t3MZYpOHMf57dz0ZlzWHnGbM5c2IEqylTfcIRQFAgEgnEImyIQCCYLYU8EU5mCbrLqhR7+/U+7ePbVQQKKxHtP7+Ajb5/NmXPqj6r007ZtXtgxyENrdvLwX3ayZa8zW/mMec188Ny5fPDcuUxvStA7kuPRF7r4/YYuntzUS0E3qYkFueT0Dt59egfVQZunX9jNH57bwbpXurEsm+p4mLef3slFZ87hwjNmM6O1drK+EkEZQigKBALBOIRNEQgEk4WwJ4LjhVd7Uvz7U7v49Zo9pPMGC9qr+cjbO7li+XTi4cBRH39Hd5L/+vMO/uuZ7X4znDPmNXP5irl84BxHNOaKBk9u7uGh5/bx+/VdJLMasZDKRae0894zp3Pm7DrWvbKfx9fu4A/P76B7IAXASdMbuHj5XC49ex5nL5lOMDBpo+Lf0gihKBAIBOMQNkUgEEwWwp4IjjeyRYP/fm4/P129k837ksRCKle8bTofPX82CzqqJ+U9dvUm+c3TlaLxzPktfPDcOb5o1A2Lp7f08fBz+3j4+X0MjBUIqjJvX9zK+86YziWndzA8muEPz23nsb9s508v7kHTTRLRECvPmM0lbzuJdy6fS0t9YlLO+a2IEIoCgUAwDmFTBALBZCHsieB4xbZtXtg9wk9X7+J/nt9P0bA4Z14jn7hwLhctaUU5wuY349nZk+S3z+zgt89s58VxovHyFXPpaEhgWhbPbx/ioef28fBz+/j/t3f3UVGWeR/AvwPjBAooL8MoalmLSNFqL77hgLjxlrmVGhxAMT0PhqZry+5zzNc0rWhRtq2jntUFbF3iPBppWc+imMqomyNoPmnmqowI8iLD4IACojRwPX+4zu6oKdHAPff0/fwlP2/n/g0y33N+zDXXVWFqhotCgXHB/nhhzIOYPOYheDzgiqKvy7Bbfw679Odwqb4JAPBUcAAmhgbh2dBheGrYALi4uNil758DDopERLdhphCRvTBPyBmYm2/gf74qx+b951FtvoaH1H0w+5lAJGqHwNP9py9LveXm0FiKHf8w4JvzJigUQPjjA5EwYRimaAPh7ekGIQS+rWjA5yUX8XlJBc5UXYFCAWiDNZgaOgQvjnkQfl5uOGmotQ6NJd9VQQgBjY8HYsYMxcRxQZgyIcRufTsrDopERLdhphCRvTBPyJlY2juw65saZO0tRYnhMjzclEjSDkHKM4EY4u9h13sZqhvx8YGz2Ko7i9LqRqiULogdNQQJEcPw3OiH4f7Azc8hnqlqxA59OXboK3Cu5gpcFAqMD7k5ND4/+kH4erqhvrEFXxYbsEt/Dl8Wl6Kx+TpaD622a7/OiIMiEdFtmClEZC/ME3JW/3fBjOx9Buw8Von2DoHYEQGYHRkI7TD1T9ot9Xa3dk/dqjuD/IOlqDW3wNO9F14cF4jEXw1DxPBBULq6QAiB05WN2K4vxw59Ocpqm+DqosCEXw7AS6FDMGnkYHh7PACLpR1HTlUi7IkhduvRWXFQJCK6DTOFiOyFeULOrraxFX8tOo+/HSyDubkNjw/uh/mxQXh+5M0Bzp7a2ztw8NtqbNOdxadfGXD1Whs0/XojLiIICRFBGBmkgUKhgBACJ8vN1ncaK0zN6OXqgsgRAZj6r6HRnktmnRUHRSKi2zBTiMhemCf0c9Ha1o4dxRexcc85lNY2YbBvb8yJDkJS2BD0ecD+x1Vcb7Ng19FybNOdxa6SC2izdGDowH6YEfUokp4JxiC/m7udCiFwvOwydhwux6dHylF1+RrcVa4w/m263XtyNl0e80tKShAaGoqioiJr7cyZM0hMTERiYiJWrlxprWdnZyMuLg7x8fE4cOAAAKCpqQmpqalISkpCSkoKGhsbAQCHDx9GXFwcEhISsGHDButjpKenIyEhAYmJiTh58mRX2yYiB8Q8ISJ7YZ4QScNd5Yrp4Q/jwKoYbJk/Dv37uWP51m8wanEB1n7+Heqbbtj1fm4qJaZoA7F12SRU5L2CP78WCY13b6zYokfQrA/x6+WfYqvuLFpvWPD0L/zwzoyROLXuJexZ9SySJwTatRenJbqgoqJCzJ07V8yfP1/s37/fWk9OThYnTpwQQgjx2muvCZ1OJy5evCimTJkibty4IS5fviyio6OFxWIR69atE1lZWUIIIT766COxZs0aIYQQEydOFDU1NaK9vV0kJCSI0tJSUVxcLFJTU4UQQpSWloq4uDibfiorK0VQUJCorKzsytMhIgk5Wp4IwUwhkivmCZFjKS41iZfX/UNoZueLIfN2iMV5x0V5XVO33vN8TYNYnasXQbM2C7fnPhD+cX8W8z7YK776rlp0dHR0672dTZfeUVSr1Vi/fj08PP69u1FbWxuqq6sxfPhwAEBkZCT0ej2Ki4sRHh4OlUoFHx8fDBw4EAaDAXq9HtHR0QCAqKgo6PV6VFZWom/fvhgw4OYZJxEREdDr9dDr9YiKigIABAYG4urVq2hubv6pMzIROQDmCRHZC/OEyLGMDvTDlt9ocXB1DCaPGoyPDpYhdNluzPnLEZyuauyWez4yoB/eSB6Lf+bMQuG7U/FC6C+wVXcWkQs/wfDUXGRsPYqLdU3dcm9n06VB0d3dHa6urja1hoYGeHl5Wb9Wq9UwmUyor6+Hj4+Pte7n53dH3c/PD3V1dTCZTD94rbe3t7Xu6+sLk8nUldaJyMEwT4jIXpgnRI4paIAX/jRrJI7+4Tm8GhOEfd/W4plVezFrw2F8U27ulnu6uCgwfvggZP0+GuUfzcZf0qIQ4NsHb+bqEfxfH3bLPZ3NfT9Zmp+fj/z8fJvaggULEB4efs9/J/61R464ba8cIYR1N6J71W65W/3W9UQkL8wTIrIX5gmR/PTv54434oZjwcRg5Ow3IGtvKZ79pga/elyD3096FKMC/brlvp69VZgR/RhmRD+G8toryNt/plvu42zuOyjGx8cjPj7+vg/k4+Nj/cA3ABiNRvj7+0Oj0eDChQs2dbVaDY1GA5PJBE9PT5tafX39HdcqlUqbel1dHfz8uucHiYi6D/OEiOyFeUIkX/36qPDfzz+G1Kih+KvuPDZ+WYrnM3TQDlPjd5MehTbYvmcx/qch/fti2bQx3fLYzsZuh5v06tULjzzyCI4dOwYA2LNnD8LDwzF27FjodDq0tbXBaDSirq4OgYGB0Gq12L17t821gwYNQnNzM6qqqmCxWFBUVAStVgutVovCwkIAwOnTp+Hv72/z+QMici7MEyKyF+YJkePydO+FBRODUfLuRKxOGAFDbRPi3juIFzJ02Pftpbu+m089p0vnKOp0OuTk5KCsrAw+Pj5Qq9XYvHkzDAYDVqxYgY6ODowYMQJLliwBAOTm5uKLL76AQqFAWloaQkND0dLSgoULF6KxsRFeXl5Yu3YtPD09cfToUWRmZgIAYmJikJKSAgDIzMzEsWPHoFAosHLlSgQHB1v74RlFRPLlaHkCMFOI5Ip5QiRv179vx9avyrFu11lUm6/hyYe98foLIZgQouGybgl0aVB0NAxhIrInZgoR2QvzhOjHa7N04BN9Bd77+z9RdfkaRgf64vUXQxAW7C91az8rdlt6SkRERERE9FOplC6YFv4wDr/9LDKmP4nK+muI++NBvJR5ACWG+vs/ANkFB0UiIiIiInI4KqULZk74BfTpz+KthBE4d+kqXsjQIen9Qzh+oXuO1aB/46BIREREREQOy62XK16JGori9Il4I+6XOFHRgOfS92PGuq/w7cUGqdtzWhwUiYiIiIjI4fV+QIn5scNQ8u5ELJ4cghJDPaLf2odXNh5BmbFJ6vacDgdFIiIiIiKSDQ+3Xkib9CiOvvscfvfrR7Hv1CWMX7kHi/KOo+7KdanbcxocFImIiIiISHa8evfCohdDcOSdiUgOfwR5hy5gzNJdyPjsFJpav5e6PdnjoEhERERERLLl39cNf5j+JA6tjkXMiAH409/PYMzSXcjaW4ob37dL3Z5scVAkIiIiIiLZe9jfA5tSx6JweSRCBvfDG9tOIOyNQnxypAIdHbI/Or7HcVAkIiIiIiKnMeIhb+T/fjy2/S4c/fqo8Juco4h+ay9039VK3ZqscFAkIiIiIiKnE/GYBoXLIrHxlTFoum5B4vv/QNIHh/DP6itStyYLHBSJiIiIiMgpubgoMHn0YBxaHYM344fjeJkZkau+lLotWVBK3QAREREREVF3eqCXK+bGBCFBOwR/+t/TUrcjC3xHkYiIiIiIfha8+6iwOuEJqduQhS4PiiUlJQgNDUVRUZG1NnfuXCQlJWHGjBmYMWMGTp06BQDIzs5GXFwc4uPjceDAAQBAU1MTUlNTkZSUhJSUFDQ2NgIADh8+jLi4OCQkJGDDhg3Wx05PT0dCQgISExNx8uTJrrZNRA6IeUJE9sI8ISKyjy4tPb148SI+/PBDPP300zb1lpYWbNq0CV5eXtZaZWUlCgoKsHXrVjQ3NyMxMRFhYWHYsmULRo8ejdmzZyMvLw9ZWVlYuHAh3n77beTk5ECj0WDatGmIjY2F2WxGRUUFtm3bBoPBgCVLliA/P/+nPXMicgjMEyKyF+YJEZH9dGlQVKvVWL9+PZYtW2ZTb2lpuePa4uJihIeHQ6VSwcfHBwMHDoTBYIBer0d6ejoAICoqCq+++ioqKyvRt29fDBgwAAAQEREBvV4Ps9mMqKgoAEBgYCCuXr2K5uZmeHh4AADa228epFlbyy1viRxR//79oVTePW4cLU8AZgqRI2OeEJG93CtPqIuDoru7+13r165dw6pVq3Dp0iUEBQVhyZIlqK+vh4+Pj/UaPz8/mEwmm7qfnx/q6upgMpnuuLayshINDQ0ICQmx1n19fWEymaxBbDKZAADTp0/vytMhom62b98+DBo06K5/52h5AjBTiBwZ84SI7OVeeUKdGBTz8/PvWEaxYMEChIeH33HtnDlzoNVqoVarsWLFCuTl5UEIYXONEAIKhcKmfrfaLXer37r+lscffxx5eXlQq9VwdXW931Mioh7Wv39/APLIE4CZQuTImCdEZC+38oTu7r6DYnx8POLj4zv1YFOmTLH+OSoqCgUFBRgzZgwuXLhgrRuNRqjVamg0GphMJnh6etrU6uvr77hWqVTa1Ovq6uDn52f92s3NDSNHjuxUj0QkHTnkCcBMIZID5gkRUfey2/EY7e3tmDlzJpqbmwHcXPs/dOhQjB07FjqdDm1tbTAajairq0NgYCC0Wi12794NANizZw/Cw8MxaNAgNDc3o6qqChaLBUVFRdBqtdBqtSgsLAQAnD59Gv7+/jbLOojIuTBPiMhemCdERF2jEHdbT3EfOp0OOTk5KCsrg4+PD9RqNTZv3oydO3diy5YtcHd3h0ajwTvvvAN3d3fk5ubiiy++gEKhQFpaGkJDQ9HS0oKFCxeisbERXl5eWLt2LTw9PXH06FFkZmYCAGJiYpCSkgIAyMzMxLFjx6BQKLBy5UoEBwdb+0lPT8eJEyegUCiwdOlSDB8+3E7fnu61Zs0afP3117BYLJgzZw5iYmKkbqnTrl+/jkmTJmH+/PmYOnWq1O10yueff47s7GwolUr89re/RUREhNQt3VdLSwsWLVqEK1eu4Pvvv8f8+fPvuqzKkZw7dw7z5s3DrFmzkJycjEuXLuH1119He3s71Go11q5dC5VKZb2eeWIfzJOeJ7dMYZ70fJ4AzBQpyDFT5JYngPwy5cfmCQEQMldcXCxSU1OFEEKUlpaKuLg4iTvqHL1eL2bPni2EEMJsNouIiAhpG/qR3nvvPTF16lSxfft2qVvpFLPZLGJiYkRTU5MwGo1i+fLlUrfUKbm5uSIzM1MIIURtba2IjY2VuKN7a2lpEcnJyWL58uUiNzdXCCHE4sWLRUFBgRBCiIyMDJGXlydli/fEPJGG3PJECHlmCvOk5zFTpCG3TJFjngghr0xxhjyRgt2WnkpFr9ffdWtqRzdq1Ch88MEHAIC+ffuitbXVuoW2ozt//jwMBgMmTJggdSudptfrERoaCg8PD/j7++Ott96SuqVO8fb2th72fPXqVXh7e0vb0H2oVCpkZWXB39/fWisuLkZkZCQAIDIyEnq9Xqr27ot50vPkmCeAPDOFedLzmCk9T46ZIsc8AeSVKc6QJ1KQ/aBYX19v84N5a2tqR+fq6orevXsDuLlz2/jx42WzG1pGRgYWL14sdRs/SlVVFYQQSEtLw7Rp02QTBpMmTUJNTQ2io6ORnJyMRYsWSd3SPSmVSri5udnUWltbrUs51Gq1Q78+mSc9T455AsgzU5gnPY+Z0vPkmClyzBNAXpniDHkiBdmfMCk6sTW1I9u7dy8++eQTbN68WepWOuWzzz7DE088gcGDB0vdyo9mNBqxfv161NTU4OWXX0ZRUZHD/6zs3LkTAQEByMnJwZkzZ7Bs2TJs375d6rZ+lP/8Ht/+enU0zJOeJec8AeSXKcyTnsdM6VlyzhS55Qkg/0yRW55IQfaD4u1bVt9ta2pHdejQIWzcuBHZ2dnw9PSUup1O0el0qKyshE6nQ21tLVQqFfr3749x48ZJ3do9+fr64sknn4RSqcSDDz6IPn36wGw2w9fXV+rW7un48eMICwsDAAQHB8NoNMJisUCplM9L193dHdevX4ebmxuMRqPNsg9HwzzpWXLNE0CemcI86XnMlJ4l10yRY54A8s8UueWJFGS/9FSuW1M3NTVhzZo12LRpE/r16yd1O532/vvvY/v27fj4448RHx+PefPmOXwAA0BYWBiOHDmCjo4OmM1mXLt2zaHX0t/y0EMP4cSJEwCA6upq9OnTRzYBfMu4ceOsr9FbW807KuZJz5JrngDyzBTmSc9jpvQsuWaKHPMEkH+myC1PpCCf/80f8NRTTyEkJASJiYnWranloKCgAA0NDUhLS7PWMjIyEBAQIF1TTkyj0SA2NhYzZ85Ea2srli9fDhcXx/89SUJCApYuXYrk5GRYLBa8+eabUrd0T6dOnUJGRgaqq6uhVCpRWFiIzMxMLF68GNu2bUNAQAAmT54sdZs/iHlCnSXHTGGe9DxmCnWGHPMEkFemOEOeSKFL5ygSERERERGR83L8X1cQERERERFRj+KgSERERERERDY4KBIREREREZENDopERERERERkg4MiERERERER2eCgSERERERERDY4KBIREREREZENDopERERERERk4/8Bw8GGDUsobEcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x504 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure G4 - Temporal evolution of the global damage function\n",
    "# Output: /figures/Figure_E4_damage_functions/damage_functions_IGIA_{specification}_{SSP}_2100-fixed-{TRUE/FALSE}.pdf\n",
    "\n",
    "powers = xr.DataArray(\n",
    "        np.arange(0, 11),\n",
    "        dims=['coeff'],\n",
    "        coords=[['cons'] + ['beta{}'.format(i) for i in range(1, 11)]])\n",
    "\n",
    "if generate_plots and scc_output == 'point-est':\n",
    "    \n",
    "    output_dir = '{}/figures/Figure_E4_damage_functions'.format(OUTPUT)\n",
    "    if not os.path.isdir(output_dir):\n",
    "        os.makedirs(output_dir)\n",
    "    \n",
    "    temps = np.linspace(-5, 20, 1101)\n",
    "    temps = xr.DataArray(temps, dims=['temp'], coords=[temps])\n",
    "\n",
    "    numvars = len(coeffs_all_years.age_adjustment)*len(coeffs_all_years.vsl_value)*len(coeffs_all_years.heterogeneity)\n",
    "    fig, axes = plt.subplots(min(2, numvars), (numvars+1)//2, figsize=(15, 7))\n",
    "\n",
    "    if numvars == 1:\n",
    "        axes = np.array([[axes]])\n",
    "\n",
    "    # dot product of coeffs and powers of temp\n",
    "    spaghetti = (\n",
    "        (coeffs_all_years.to_array('coeff') * (temps ** powers)).sum(dim='coeff'))\n",
    "\n",
    "    if 1 == 1:\n",
    "        current_row = 0\n",
    "        for hi, h in enumerate(coeffs_all_years.heterogeneity.values):\n",
    "            current_col = -1\n",
    "            if hi == 1:\n",
    "                current_row = 1\n",
    "            for ai, a in enumerate(coeffs_all_years.age_adjustment.values):\n",
    "                for vi, v in enumerate(coeffs_all_years.vsl_value.values):\n",
    "                    current_col += 1\n",
    "                    lines = []\n",
    "                    for y in reversed(coeffs_all_years.year.values[:86:5]):\n",
    "                        #print(current_row, current_col)\n",
    "                        lines.append(\n",
    "                            axes[current_row, current_col].plot(\n",
    "                                temps.values,\n",
    "                                spaghetti.sel(vsl_value=v, age_adjustment=a, heterogeneity=h, year=y, variable=ssp).values,\n",
    "                                color=matplotlib.cm.Blues_r((y-2015.0)/(2100-2000)),\n",
    "                                label=int(y)))\n",
    "\n",
    "                    axes[current_row, current_col].set_facecolor('white')\n",
    "                    axes[current_row, current_col].set_title('{}, {}, {}, {}'.format(v, a, h, ssp))\n",
    "                    axes[current_row, current_col].set_xbound(lower=0, upper=10)\n",
    "                    axes[current_row, current_col].set_ybound(lower=-150000, upper=250000)\n",
    "\n",
    "        plt.subplots_adjust(right=0.85)\n",
    "        axes[0, -1].legend(\n",
    "            list(reversed(lines))[::5],\n",
    "            labels=list(reversed(coeffs_all_years.year.values[:86]))[::5],\n",
    "            loc='center left',\n",
    "            bbox_to_anchor=(1.01, (1-(((numvars-1)//2)%2))*0.5))\n",
    "        fig.set_facecolor('white')\n",
    "\n",
    "        sns.despine()\n",
    "        fig.savefig(os.path.join(output_dir, 'damage_functions_IGIA_{}_{}_{}_2100-fixed-{}.pdf'\n",
    "                    .format(specification, ssp, version, hold_2100_damages_fixed)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if generate_plots and scc_output == 'point-est':\n",
    "    temps = np.linspace(-5, 15, 1101)\n",
    "    temps = xr.DataArray(temps, dims=['temp'], coords=[temps])\n",
    "\n",
    "    numvars = len(coeffs_all_years.age_adjustment)*len(coeffs_all_years.vsl_value)*len(coeffs_all_years.heterogeneity)\n",
    "    fig, axes = plt.subplots(min(2, numvars), (numvars+1)//2, figsize=(15, 7))\n",
    "\n",
    "    if numvars == 1:\n",
    "        axes = np.array([[axes]])\n",
    "\n",
    "    # dot product of coeffs and powers of temp\n",
    "    spaghetti = (\n",
    "        (coeffs_all_years.to_array('coeff') * (temps ** powers)).sum(dim='coeff'))\n",
    "\n",
    "    if 1 == 1:\n",
    "        current_row = 0\n",
    "        for hi, h in enumerate(coeffs_all_years.heterogeneity.values):\n",
    "            current_col = -1\n",
    "            if hi == 1:\n",
    "                current_row = 1\n",
    "            for ai, a in enumerate(coeffs_all_years.age_adjustment.values):\n",
    "                for vi, v in enumerate(coeffs_all_years.vsl_value.values):\n",
    "                    current_col += 1\n",
    "                    lines = []\n",
    "                    for y in reversed(coeffs_all_years.year.values[85::10]):\n",
    "                        #print(current_row, current_col)\n",
    "                        lines.append(\n",
    "                            axes[current_row, current_col].plot(\n",
    "                                temps.values,\n",
    "                                spaghetti.sel(vsl_value=v, age_adjustment=a, heterogeneity=h, year=y, variable=ssp).values,\n",
    "                                color=matplotlib.cm.Reds_r((y-2100.0)/(2300-2100)),\n",
    "                                label=int(y)))\n",
    "\n",
    "                    axes[current_row, current_col].set_facecolor('white')\n",
    "                    axes[current_row, current_col].set_title('{}, {}, {}, {}'.format(v, a, h, ssp))\n",
    "                    axes[current_row, current_col].set_xbound(lower=0, upper=10)\n",
    "                    axes[current_row, current_col].set_ybound(lower=-200000, upper=300000)\n",
    "\n",
    "        plt.subplots_adjust(right=0.85)\n",
    "        axes[0, -1].legend(\n",
    "            list(reversed(lines))[::5],\n",
    "            labels=list(reversed(coeffs_all_years.year.values[85:]))[::10],\n",
    "            loc='center left',\n",
    "            bbox_to_anchor=(1.01, (1-(((numvars-1)//2)%2))*0.5))\n",
    "        fig.set_facecolor('white')\n",
    "\n",
    "        sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute damages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the damage function coefficients with the FAIR temperatures:\n",
    "\n",
    "$$d_y = c_2*T_y^2+C_1*T_y+C_0 \\hspace{1in}\\forall \\hspace{0.1in} y \\in [2020, 2300]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "damages = (\n",
    "    (coeffs_all_years.to_array('coeff') * (fair_temperatures_anomaly ** powers)).sum(dim='coeff'))\n",
    "\n",
    "# Fix the coordinate re-order bug introduced by holding the damage function constant post-2100.\n",
    "if scc_output == 'point-est':\n",
    "    damages = damages.transpose('variable', 'year', 'pulse', 'rcp', 'age_adjustment', 'vsl_value', 'heterogeneity')\n",
    "elif scc_output == 'df-uncertainty':\n",
    "    damages = damages.transpose('variable', 'year', 'pulse', 'rcp', 'age_adjustment', 'vsl_value', 'heterogeneity', 'pctile')\n",
    "else:\n",
    "    raise NotImplementedError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot time series of damages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "if generate_plots and scc_output == 'point-est':\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "    colors = ['green', 'blue', 'red', 'black']\n",
    "    styles = ['solid', 'dashed']\n",
    "\n",
    "    lines = []\n",
    "    labels = []\n",
    "    for r, rcp in enumerate(damages.rcp.values):\n",
    "        for p, pulse in enumerate(damages.pulse.values):\n",
    "            labels.append('{}{}'.format(rcp, ['', '+'][p]))\n",
    "            lines.append(\n",
    "                ax.plot(\n",
    "                    damages.year,\n",
    "                    damages.sel(rcp=rcp, pulse=pulse, vsl_value='epa', age_adjustment='vly', heterogeneity='popavg', variable=ssp).T,\n",
    "                    color=colors[r],\n",
    "                    linestyle=styles[p])[0])\n",
    "\n",
    "    plt.legend(lines, labels)\n",
    "    ax.axes.set_title(\n",
    "        'Time series of damages by scenario (Billion USD), Popavg VLY, EPA VSL',\n",
    "        size=14)\n",
    "\n",
    "    sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot time series of mortality damages by RCP and VSL measure (Billion USD)\n",
    "Note that this is in Billion USD_2019, and is the damages from the 1GtC pulse. Previously, damages had been converted to a \\$/tCO2 basis (or Billion \\$/GtCO2). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "if generate_plots and scc_output == 'point-est':\n",
    "\n",
    "    plotted = (\n",
    "        (damages.sel(variable=ssp, rcp=['rcp45', 'rcp85']))\n",
    "        .diff(dim='pulse').sel(pulse='pulse'))\n",
    "\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(12, 10))\n",
    "    for r, rcp in enumerate(plotted.rcp):\n",
    "        labels = []\n",
    "        lines = []\n",
    "        ii = 0\n",
    "        for a, adj in enumerate(plotted.age_adjustment.values):\n",
    "            for v, vsl in enumerate(plotted.vsl_value.values):\n",
    "                for h, heterogeneity in enumerate(plotted.heterogeneity.values):\n",
    "                    labels.append((adj, vsl, heterogeneity))\n",
    "                    lines.append(\n",
    "                        axes[r%2].plot(\n",
    "                            plotted.year.values,\n",
    "                            (\n",
    "                                plotted\n",
    "                                .sel(\n",
    "                                    rcp=rcp,\n",
    "                                    age_adjustment=adj,\n",
    "                                    vsl_value=vsl,\n",
    "                                    heterogeneity=heterogeneity)),\n",
    "                            color=['lightblue', 'blue', 'lightgreen', 'green','lightcoral','red'][ii],\n",
    "                            linestyle=('dashed' if h == 0 else 'solid'),\n",
    "                            label='{}, {}, {}'.format(adj, vsl, heterogeneity))[0])\n",
    "                    ii += 1\n",
    "\n",
    "        axes[r%2].set_title('Damages by VSL measure, {}'.format(rcp.data))\n",
    "        axes[r%2].set_facecolor('white')\n",
    "\n",
    "    axes[1].legend(\n",
    "        lines,\n",
    "        ['{} {} {}'.format(*tuple(s)) for s in labels],\n",
    "        bbox_to_anchor=(1.1, 1),\n",
    "        loc='center left')\n",
    "\n",
    "    fig.suptitle('Mortality damages for SSP3 by RCP and VSL measure (Billion USD_2019 from 1GtC pulse)', size=18)\n",
    "    fig.set_facecolor('white')\n",
    "    plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
    "\n",
    "    sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compue the SCC (NPV of damages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "discount the time series of damages using the formula\n",
    "\n",
    "$$SCC=\\sum_{y\\in\\left[2020, 2300\\right]}{\\frac{d_y}{\\left({1+r}\\right)^{y-2020}}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the time series of discounted damages. Then plot time series of mortality damages by RCP and VSL measure (Billion USD), discounted to present value and per ton CO2. \n",
    "\n",
    "Note that the plots are in Billion USD_2019, discounted to 2020, for RCP 8.5, with EPA VSL, value of life years, and globally varying income ('scaled') under SSP3. We want plots evaluated for discount rates 2.5%, 3%, and 5%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "discrates = [1., 1.5, 2., 2.5, 3., 5., 7.]\n",
    "\n",
    "discdata = []\n",
    "for r in discrates:\n",
    "    discdata.append(damages / (1+r/100)**(damages.year - 2020))\n",
    "\n",
    "current_equivalent_damages = xr.concat(discdata, dim=pd.Index(discrates, name='discrate'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Useful for diagnostics.\n",
    "\n",
    "plotted_npv = (\n",
    "    (current_equivalent_damages.sel(\n",
    "        variable=ssp, rcp=['rcp45'], age_adjustment='vly', vsl_value='epa', heterogeneity='scaled', discrate=3.0))\n",
    "    .diff(dim='pulse').sel(pulse='pulse') * MAGNITUDE_OF_DAMAGES * CONVERSION)\n",
    "plotted_npv = plotted_npv.to_dataframe(\"dr3\")\n",
    "\n",
    "plotted_damages = (\n",
    "    (damages.sel(\n",
    "        variable=ssp, rcp=['rcp45'], age_adjustment='vly', vsl_value='epa', heterogeneity='scaled'))\n",
    "    .diff(dim='pulse').sel(pulse='pulse') * MAGNITUDE_OF_DAMAGES * CONVERSION)\n",
    "plotted_damages = plotted_damages.to_dataframe(\"dr0\")\n",
    "\n",
    "plotted_df = plotted_npv.merge(plotted_damages, on=['year','rcp'])[['dr3','dr0']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if generate_plots and scc_output == 'point-est':\n",
    "    discrates_to_plot = [2.5, 3., 5.]\n",
    "    \n",
    "    a='vly'\n",
    "    v='epa'\n",
    "    h='scaled'\n",
    "    \n",
    "    plotted = (\n",
    "        (current_equivalent_damages.sel(\n",
    "            variable=ssp, rcp=['rcp45', 'rcp85'], age_adjustment=a, vsl_value=v, heterogeneity=h))\n",
    "        .diff(dim='pulse').sel(pulse='pulse') * MAGNITUDE_OF_DAMAGES * CONVERSION)\n",
    "\n",
    "    fig, axes = plt.subplots(2, 1, figsize=(12, 10))\n",
    "    for r, rcp in enumerate(plotted.rcp):\n",
    "        labels = []\n",
    "        lines = []\n",
    "        for dr in discrates_to_plot:\n",
    "            labels.append((dr))\n",
    "\n",
    "            lines.append(\n",
    "                axes[r%2].plot(\n",
    "                    plotted.year.values,\n",
    "                    (\n",
    "                        plotted\n",
    "                        .sel(\n",
    "                            rcp=rcp,\n",
    "                            discrate=dr)),\n",
    "                    #color=['lightblue', 'blue', 'lightgreen', 'green'][discrates_to_plot.index(dr)],\n",
    "                    color=matplotlib.cm.Blues_r(float(discrates_to_plot.index(dr))/len(discrates_to_plot)),\n",
    "                    linestyle=('solid'),\n",
    "                    label='{}'.format(dr))[0])\n",
    "\n",
    "        axes[r%2].set_title('Present value of damages ($/ton CO2), {}'.format(rcp.data))\n",
    "        axes[r%2].set_facecolor('white')\n",
    "\n",
    "    axes[1].legend(\n",
    "        lines,\n",
    "        ['{}'.format(s) for s in labels],\n",
    "        bbox_to_anchor=(1.1, 1),\n",
    "        loc='center left')\n",
    "\n",
    "    fig.suptitle('Present value of mortality damages for {} by RCP and discount rate - {}-{}-{}'.format(ssp, a, v, h), size=18)\n",
    "    fig.set_facecolor('white')\n",
    "    plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
    "\n",
    "    sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the SCC (sum of discounted marginal damage time series) by discount rate and RCP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "scc = (\n",
    "    (current_equivalent_damages.diff(dim='pulse').sel(pulse='pulse') * MAGNITUDE_OF_DAMAGES * CONVERSION)\n",
    "    .sum(dim='year'))\n",
    "\n",
    "scc_pre_2100 = (\n",
    "    (current_equivalent_damages.sel(year=slice(2020, 2099)).diff(dim='pulse')\n",
    "    .sel(pulse='pulse') * MAGNITUDE_OF_DAMAGES * CONVERSION)\n",
    "    .sum(dim='year'))\n",
    "\n",
    "scc_post_2100 = (\n",
    "    (current_equivalent_damages.sel(year=slice(2100, 3000)).diff(dim='pulse')\n",
    "    .sel(pulse='pulse') * MAGNITUDE_OF_DAMAGES * CONVERSION)\n",
    "    .sum(dim='year'))\n",
    "\n",
    "\n",
    "scc = xr.concat([scc, scc_pre_2100, scc_post_2100], pd.Index(['all', 'pre2100', 'post2100'], name='time_cut'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (variable: 1, age_adjustment: 3, vsl_value: 1)&gt;\n",
       "array([[[14.32892487],\n",
       "        [14.18665438],\n",
       "        [22.14345164]]])\n",
       "Coordinates:\n",
       "  * variable        (variable) object &#x27;SSP3&#x27;\n",
       "  * age_adjustment  (age_adjustment) object &#x27;mt&#x27; &#x27;vly&#x27; &#x27;vsl&#x27;\n",
       "  * vsl_value       (vsl_value) object &#x27;epa&#x27;\n",
       "    heterogeneity   &lt;U6 &#x27;scaled&#x27;\n",
       "    pulse           &lt;U5 &#x27;pulse&#x27;\n",
       "    rcp             &lt;U5 &#x27;rcp85&#x27;\n",
       "    discrate        float64 3.0\n",
       "    time_cut        &lt;U3 &#x27;all&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>variable</span>: 1</li><li><span class='xr-has-index'>age_adjustment</span>: 3</li><li><span class='xr-has-index'>vsl_value</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-728461b1-d92e-4b53-8ca6-521dc4b49034' class='xr-array-in' type='checkbox' checked><label for='section-728461b1-d92e-4b53-8ca6-521dc4b49034' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>14.33 14.19 22.14</span></div><div class='xr-array-data'><pre>array([[[14.32892487],\n",
       "        [14.18665438],\n",
       "        [22.14345164]]])</pre></div></div></li><li class='xr-section-item'><input id='section-e6e67747-e5a1-463f-bf4d-b695828b6366' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6e67747-e5a1-463f-bf4d-b695828b6366' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variable</span></div><div class='xr-var-dims'>(variable)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SSP3&#x27;</div><input id='attrs-8df5f040-9b77-42e8-992b-79b8e9bec8f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8df5f040-9b77-42e8-992b-79b8e9bec8f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42800004-483a-4363-85d9-f69cf7e15289' class='xr-var-data-in' type='checkbox'><label for='data-42800004-483a-4363-85d9-f69cf7e15289' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SSP3&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>age_adjustment</span></div><div class='xr-var-dims'>(age_adjustment)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;mt&#x27; &#x27;vly&#x27; &#x27;vsl&#x27;</div><input id='attrs-c8eda2f6-27f8-4a9d-91cf-e56cb94ab845' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8eda2f6-27f8-4a9d-91cf-e56cb94ab845' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89f57522-844d-4c5f-8a9e-a99d8343b83c' class='xr-var-data-in' type='checkbox'><label for='data-89f57522-844d-4c5f-8a9e-a99d8343b83c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;mt&#x27;, &#x27;vly&#x27;, &#x27;vsl&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vsl_value</span></div><div class='xr-var-dims'>(vsl_value)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;epa&#x27;</div><input id='attrs-e62c1121-da5c-4865-ba50-d75501ff84d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e62c1121-da5c-4865-ba50-d75501ff84d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6b4956f-6d0c-4b70-bcc9-45a5ce5afd12' class='xr-var-data-in' type='checkbox'><label for='data-e6b4956f-6d0c-4b70-bcc9-45a5ce5afd12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;epa&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heterogeneity</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;scaled&#x27;</div><input id='attrs-b6c06e67-bea1-404e-bbef-7d9e6868bf79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b6c06e67-bea1-404e-bbef-7d9e6868bf79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-123fe8a0-9b0d-4a2a-947b-0293c3b71244' class='xr-var-data-in' type='checkbox'><label for='data-123fe8a0-9b0d-4a2a-947b-0293c3b71244' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;scaled&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulse</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;pulse&#x27;</div><input id='attrs-4ff2bfbf-d8ad-46b9-9e47-b82e6eecfc87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ff2bfbf-d8ad-46b9-9e47-b82e6eecfc87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45a36f38-65c1-4378-a5c2-d9b607b782cb' class='xr-var-data-in' type='checkbox'><label for='data-45a36f38-65c1-4378-a5c2-d9b607b782cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;pulse&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rcp</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;rcp85&#x27;</div><input id='attrs-30619585-930b-4548-9b5e-4a5dc22ec2dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30619585-930b-4548-9b5e-4a5dc22ec2dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-209215e1-62fc-4cb6-a8a8-7f7f6f555bd5' class='xr-var-data-in' type='checkbox'><label for='data-209215e1-62fc-4cb6-a8a8-7f7f6f555bd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;rcp85&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>discrate</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0</div><input id='attrs-7d470c70-4d98-421f-8668-597f8b51cd03' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d470c70-4d98-421f-8668-597f8b51cd03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89c98880-e322-4cfd-82bf-49efc90d39a7' class='xr-var-data-in' type='checkbox'><label for='data-89c98880-e322-4cfd-82bf-49efc90d39a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(3.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_cut</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;all&#x27;</div><input id='attrs-3015c809-22b0-4484-b83d-ad87a9074ab7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3015c809-22b0-4484-b83d-ad87a9074ab7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ec699d6-f7e1-40fc-bbf7-3fde4ef06143' class='xr-var-data-in' type='checkbox'><label for='data-1ec699d6-f7e1-40fc-bbf7-3fde4ef06143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;all&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a4665da-26e2-44a4-af82-3c18c62975df' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a4665da-26e2-44a4-af82-3c18c62975df' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (variable: 1, age_adjustment: 3, vsl_value: 1)>\n",
       "array([[[14.32892487],\n",
       "        [14.18665438],\n",
       "        [22.14345164]]])\n",
       "Coordinates:\n",
       "  * variable        (variable) object 'SSP3'\n",
       "  * age_adjustment  (age_adjustment) object 'mt' 'vly' 'vsl'\n",
       "  * vsl_value       (vsl_value) object 'epa'\n",
       "    heterogeneity   <U6 'scaled'\n",
       "    pulse           <U5 'pulse'\n",
       "    rcp             <U5 'rcp85'\n",
       "    discrate        float64 3.0\n",
       "    time_cut        <U3 'all'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you may use this to filter/view specific SCCs - only use if point estimate results\n",
    "# df-quantiles still needs smoothing step below\n",
    "scc.sel(time_cut='all').sel(rcp='rcp85').sel(discrate=3).sel(heterogeneity='scaled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export point estimate SCC to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:02:24.413981\n"
     ]
    }
   ],
   "source": [
    "if scc_output == 'point-est':\n",
    "\n",
    "    (   scc\n",
    "        .to_series()\n",
    "        .unstack('rcp')\n",
    "        .to_csv('{}/scc_{}_{}_pulse{}GtC_{}_median_IGIA_2100-fixed-{}_MC.csv'.format(OUTPUT_data, specification, ssp, PULSE_AMT, version, hold_2100_damages_fixed))  ) \n",
    "\n",
    "    print(datetime.now() - startTime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appended AG SCC\n",
      "Saved: scc_mortality_quadratic_IGIA_SSP3_pulse1.0GtC_v0.5_median_IGIA_2100-fixed-False_MC_ag02.csv\n"
     ]
    }
   ],
   "source": [
    "# save a file with appended Ashenfelter_Greenstone 2002 conversion\n",
    "if scc_output == 'point-est':\n",
    "    scale_ag02_scc.Ashenfelter_Greenstone(OUTPUT_data,\n",
    "        'scc_{}_{}_pulse{}GtC_{}_median_IGIA_2100-fixed-{}_MC.csv'.format(specification, ssp, PULSE_AMT, version, hold_2100_damages_fixed))"
   ]
  },
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   "metadata": {},
   "source": [
    "if you are running `point-est`, the workflow ends here"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Damage function uncertainty "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot histogram of quantiles of the SCC, upweighting each quantile by its associated mass in the economic uncertainty distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New quantiles are [0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55,\n",
    "       0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95], probability weights are each 0.05 except for the 5th and 95th quantiles, which are weighted 0.075 to account for the fact that they cover more of the range than all other quantiles (0 to .075 and .925 to 1, respectively). Note that these weights are calculated with the function get_weights, rather than being hard-coded as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_weights(quantiles):\n",
    "    \"\"\"\n",
    "        Compute the weights of each quantile regression.\n",
    "        `quantiles` must be between 0-1! \n",
    "    \"\"\"\n",
    "    quantiles = np.array(quantiles)\n",
    "    # find midpoints between quantiles\n",
    "    bounds = np.array([0] + ((quantiles[:-1] + quantiles[1:]) / 2).tolist() + [1])\n",
    "    if (bounds > 1).any():\n",
    "        raise RuntimeError('quantiles must be between 0-1')\n",
    "    weights = np.diff(bounds)\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "if scc_output == 'df-uncertainty':\n",
    "    weights = get_weights(scc.pctile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "if scc_output == 'df-uncertainty':\n",
    "    fig, axes = plt.subplots(6, 2, figsize=(40, 25))\n",
    "    #fig, axes = plt.subplots(4, 3, figsize=(8, 5))\n",
    "\n",
    "    # it = lambda x: tqdm.tqdm_notebook(list(enumerate(x.values)), leave=False)\n",
    "    it = lambda x: list(enumerate(x.values))\n",
    "\n",
    "    plotting = scc.sel(rcp=['rcp45', 'rcp85'], discrate=3.0)\n",
    "\n",
    "    weights = np.array(weights)\n",
    "    hist_bins = range(-100, 310, 10)\n",
    "    ii = 0\n",
    "\n",
    "    for ri, rcp in it(plotting.rcp):\n",
    "        for ai, age in it(plotting.age_adjustment):\n",
    "            for vi, vsl in it(plotting.vsl_value):\n",
    "                for hi, het in it(plotting.heterogeneity):\n",
    "                    #for di, disc in it(plotting.discrate):\n",
    "\n",
    "                    print(ii, rcp, age, vsl, het)\n",
    "                    slicers = dict(\n",
    "                        #discrate=disc,\n",
    "                        rcp=rcp,\n",
    "                        age_adjustment=age,\n",
    "                        vsl_value=vsl,\n",
    "                        heterogeneity=het,\n",
    "                        time_cut='all')\n",
    "\n",
    "                    this = plotting.sel(**slicers)\n",
    "\n",
    "                    ax = axes[ri*3+ai, vi*2+hi]\n",
    "                    #print(this.values)\n",
    "                    #print('di is: {}'.format(di))\n",
    "\n",
    "                    #mask = (this.values > 0)\n",
    "                    ax.hist(\n",
    "                        this.values[0,:],\n",
    "                        #np.log(this.values[mask]),\n",
    "                        bins=hist_bins,\n",
    "                        density=True,\n",
    "                        color=['red', 'green', 'blue','orange','yellow','purple'][2],\n",
    "                        alpha=0.45,\n",
    "                        weights=weights)\n",
    "\n",
    "                    #ax.set_xticklabels(['$10^{{{:0.0f}}}$'.format(i) for i in ax.get_xticks()])\n",
    "                    ax.set_title('{} {} {} {} 3%disc'.format(rcp,age,vsl,het))\n",
    "                    ii += 1\n",
    "\n",
    "    fig.suptitle('SCC - Economic Uncertainty', size=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "if scc_output == 'df-uncertainty':\n",
    "    # Save the IQRs resulting from the 25,75 ptiles of the damage function\n",
    "    dmg_IQRs = scc.sel(pctile=[0.25,0.75],time_cut='all')\n",
    "\n",
    "    # Save IQRs from fitting a distribution of type fit_type\n",
    "    my_IQRs = scc.sel(pctile=[0.25,0.75],time_cut='all')\n",
    "\n",
    "    fit_type = 'norm' #'lognorm' or 'norm' - 'norm' is standard\n",
    "\n",
    "    for ri, rcp in it(scc.rcp):\n",
    "        for ai, age in it(scc.age_adjustment):\n",
    "            for vi, vsl in it(scc.vsl_value):\n",
    "                for hi, het in it(scc.heterogeneity):\n",
    "                    for di, disc in it(scc.discrate):\n",
    "\n",
    "                        slicers = dict(\n",
    "                            discrate=disc,\n",
    "                            rcp=rcp,\n",
    "                            age_adjustment=age,\n",
    "                            vsl_value=vsl,\n",
    "                            heterogeneity=het,\n",
    "                            time_cut='all')\n",
    "\n",
    "                        this = scc.sel(**slicers)\n",
    "\n",
    "                        if fit_type == 'lognorm':\n",
    "                            # Fit a lognormal distribution\n",
    "                            # Helpful: https://stackoverflow.com/questions/51410155/proper-way-to-fit-a-lognormal-distribution-with-weight-in-python\n",
    "                            logs = np.log(this.values[0,:])\n",
    "                            muhat = np.average(logs, weights=weights)\n",
    "                            # varhat is the weighted variance of ln(x).  There isn't a function in\n",
    "                            # numpy for the weighted variance, so we compute it using np.average.\n",
    "                            varhat = np.average((logs - muhat)**2, weights=weights)\n",
    "\n",
    "                            shape = np.sqrt(varhat)\n",
    "                            scale = np.exp(muhat)\n",
    "                            #print('{} {}'.format(shape, scale))\n",
    "\n",
    "                            if ~np.isnan(shape):\n",
    "                                randoms = lognorm.rvs(s=shape, scale=scale, size=1000)\n",
    "                                pt = np.percentile(randoms,[25, 75])\n",
    "                            else:\n",
    "                                pt = np.array([np.nan, np.nan])\n",
    "                        else:\n",
    "                            # Fit a normal distribution\n",
    "                            muhat = np.average(this.values[0,:], weights=weights)\n",
    "                            varhat = np.average((this.values[0,:] - muhat)**2, weights=weights)\n",
    "                            scale = np.sqrt(varhat)\n",
    "                            randoms = norm.rvs(loc=muhat, scale=scale, size=1000)\n",
    "                            pt = np.percentile(randoms,[25, 75])\n",
    "\n",
    "\n",
    "                        iqr_slicers = dict(\n",
    "                            discrate=disc,\n",
    "                            rcp=rcp,\n",
    "                            age_adjustment=age,\n",
    "                            vsl_value=vsl,\n",
    "                            heterogeneity=het)\n",
    "\n",
    "                        my_IQRs.loc[iqr_slicers] = pt\n",
    "\n",
    "                        # Add the SCCs associated with the 25 and 75 quantile damage functions\n",
    "                        tmp = scc.sel(**slicers)\n",
    "                        dmg_IQRs.loc[iqr_slicers] = tmp.sel(pctile=[0.25,0.75])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
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       "\n",
       "\n",
       "       [[[-10.37222094,  70.27449836]]],\n",
       "\n",
       "\n",
       "       [[[ -9.91178224,  33.03655914]]],\n",
       "\n",
       "\n",
       "       [[[ -5.38197171,  14.31832161]]]])\n",
       "Coordinates:\n",
       "  * variable        (variable) object &#x27;SSP3&#x27;\n",
       "    age_adjustment  &lt;U2 &#x27;mt&#x27;\n",
       "  * vsl_value       (vsl_value) object &#x27;epa&#x27;\n",
       "    heterogeneity   &lt;U6 &#x27;scaled&#x27;\n",
       "  * pctile          (pctile) float64 0.25 0.75\n",
       "    pulse           &lt;U5 &#x27;pulse&#x27;\n",
       "    rcp             &lt;U5 &#x27;rcp85&#x27;\n",
       "  * discrate        (discrate) float64 1.5 2.0 3.0 5.0\n",
       "    time_cut        &lt;U3 &#x27;all&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>discrate</span>: 4</li><li><span class='xr-has-index'>variable</span>: 1</li><li><span class='xr-has-index'>vsl_value</span>: 1</li><li><span class='xr-has-index'>pctile</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8734a937-e7f6-478e-9ec5-090f77b236a4' class='xr-array-in' type='checkbox' checked><label for='section-8734a937-e7f6-478e-9ec5-090f77b236a4' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-2.269 115.0 -10.37 70.27 -9.912 33.04 -5.382 14.32</span></div><div class='xr-array-data'><pre>array([[[[ -2.2690497 , 115.03153189]]],\n",
       "\n",
       "\n",
       "       [[[-10.37222094,  70.27449836]]],\n",
       "\n",
       "\n",
       "       [[[ -9.91178224,  33.03655914]]],\n",
       "\n",
       "\n",
       "       [[[ -5.38197171,  14.31832161]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-fdd2357b-d7ec-4723-839f-8b447a086ecf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fdd2357b-d7ec-4723-839f-8b447a086ecf' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variable</span></div><div class='xr-var-dims'>(variable)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;SSP3&#x27;</div><input id='attrs-133620c3-eb02-4fb9-a085-ff5978105d1b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-133620c3-eb02-4fb9-a085-ff5978105d1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b5de55b-25a0-4b89-8092-779f3bf3d76e' class='xr-var-data-in' type='checkbox'><label for='data-7b5de55b-25a0-4b89-8092-779f3bf3d76e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SSP3&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>age_adjustment</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;mt&#x27;</div><input id='attrs-9dd950df-2a53-49b1-aed4-abb1746c2598' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9dd950df-2a53-49b1-aed4-abb1746c2598' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d577aa3-3ed0-4cbd-b8dc-353d88952220' class='xr-var-data-in' type='checkbox'><label for='data-6d577aa3-3ed0-4cbd-b8dc-353d88952220' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;mt&#x27;, dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vsl_value</span></div><div class='xr-var-dims'>(vsl_value)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;epa&#x27;</div><input id='attrs-3f2b941a-8918-4409-8a12-908462366389' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f2b941a-8918-4409-8a12-908462366389' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05eafcbf-723c-44b6-be69-3107b991a9a5' class='xr-var-data-in' type='checkbox'><label for='data-05eafcbf-723c-44b6-be69-3107b991a9a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;epa&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heterogeneity</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;scaled&#x27;</div><input id='attrs-4e1e0520-920f-45fd-98e1-0d11e6e1b010' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e1e0520-920f-45fd-98e1-0d11e6e1b010' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abf1e119-c8af-44da-98a0-c965918e9622' class='xr-var-data-in' type='checkbox'><label for='data-abf1e119-c8af-44da-98a0-c965918e9622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;scaled&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pctile</span></div><div class='xr-var-dims'>(pctile)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.25 0.75</div><input id='attrs-12028423-768b-46be-b6eb-008a4d5f63a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12028423-768b-46be-b6eb-008a4d5f63a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5ccf90f-9819-44d4-b93f-b63432b01d1f' class='xr-var-data-in' type='checkbox'><label for='data-c5ccf90f-9819-44d4-b93f-b63432b01d1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.25, 0.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulse</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;pulse&#x27;</div><input id='attrs-1ce5e93a-098f-4684-9bca-6e143847247c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ce5e93a-098f-4684-9bca-6e143847247c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2da9ec04-7620-4cba-a836-9f2d192fdc6b' class='xr-var-data-in' type='checkbox'><label for='data-2da9ec04-7620-4cba-a836-9f2d192fdc6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;pulse&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rcp</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;rcp85&#x27;</div><input id='attrs-f63f517e-28b0-4d47-b0bf-5d9d6e5542f4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f63f517e-28b0-4d47-b0bf-5d9d6e5542f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3a51636-d2dc-4b62-84d0-10d101baef84' class='xr-var-data-in' type='checkbox'><label for='data-c3a51636-d2dc-4b62-84d0-10d101baef84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;rcp85&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>discrate</span></div><div class='xr-var-dims'>(discrate)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 2.0 3.0 5.0</div><input id='attrs-ee9d415e-639d-4be5-a742-07de3c9f1827' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee9d415e-639d-4be5-a742-07de3c9f1827' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16f8ad23-b552-4013-8e1c-938fd1495b96' class='xr-var-data-in' type='checkbox'><label for='data-16f8ad23-b552-4013-8e1c-938fd1495b96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.5, 2. , 3. , 5. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_cut</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;all&#x27;</div><input id='attrs-056ab8d9-b130-404a-80f2-b4aa9a4b0104' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-056ab8d9-b130-404a-80f2-b4aa9a4b0104' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10fab8ef-66ce-4c45-9a44-b9aa671214f1' class='xr-var-data-in' type='checkbox'><label for='data-10fab8ef-66ce-4c45-9a44-b9aa671214f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;all&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54e75584-3bf4-4eae-9dcb-d2c196536af3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-54e75584-3bf4-4eae-9dcb-d2c196536af3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (discrate: 4, variable: 1, vsl_value: 1, pctile: 2)>\n",
       "array([[[[ -2.2690497 , 115.03153189]]],\n",
       "\n",
       "\n",
       "       [[[-10.37222094,  70.27449836]]],\n",
       "\n",
       "\n",
       "       [[[ -9.91178224,  33.03655914]]],\n",
       "\n",
       "\n",
       "       [[[ -5.38197171,  14.31832161]]]])\n",
       "Coordinates:\n",
       "  * variable        (variable) object 'SSP3'\n",
       "    age_adjustment  <U2 'mt'\n",
       "  * vsl_value       (vsl_value) object 'epa'\n",
       "    heterogeneity   <U6 'scaled'\n",
       "  * pctile          (pctile) float64 0.25 0.75\n",
       "    pulse           <U5 'pulse'\n",
       "    rcp             <U5 'rcp85'\n",
       "  * discrate        (discrate) float64 1.5 2.0 3.0 5.0\n",
       "    time_cut        <U3 'all'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you may use this to filter/view specific SCCs - only use if df-uncertainty results\n",
    "my_IQRs.sel(heterogeneity='scaled').sel(discrate=[1.5, 2, 3, 5]).sel(rcp='rcp85').sel(age_adjustment='mt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "if scc_output == 'df-uncertainty':\n",
    "    (\n",
    "        my_IQRs\n",
    "        .to_series()\n",
    "        .unstack('rcp')\n",
    "        .to_csv('{}/uncertainty/scc_IQRs_{}_{}_{}_pulse{}GtC_{}_2100-fixed-{}_v2.csv'.format(OUTPUT_data, fit_type, specification, ssp, PULSE_AMT, version, hold_2100_damages_fixed)))\n",
    "\n",
    "    (\n",
    "        dmg_IQRs\n",
    "        .to_series()\n",
    "        .unstack('rcp')\n",
    "        .to_csv('{}/uncertainty/scc_IQRs_dmg-mapping_{}_{}_pulse{}GtC_{}_2100-fixed-{}.csv'.format(OUTPUT_data, specification, ssp, PULSE_AMT, version, hold_2100_damages_fixed)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save a file with appended Ashenfelter_Greenstone 2002 conversion\n",
    "if scc_output == 'df-uncertainty':\n",
    "    scale_ag02_scc.Ashenfelter_Greenstone('{}/uncertainty/'.format(OUTPUT_data),\n",
    "        'scc_IQRs_{}_{}_{}_pulse{}GtC_{}_2100-fixed-{}_v2.csv'.format(fit_type, specification, ssp, PULSE_AMT, version, hold_2100_damages_fixed))"
   ]
  }
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